Improving Rank Algorithm of Search Engine with

Ontology and Categorization

By

Qiaowei Dai

A thesis submitted for the degree of

Master of Science (Computer and Information Science)

School of Computer and Information Science

Division of Information Technology, Engineering and the Environment

SupervisorDr. Jiuyong Li

1st June 2009

University of South Australia

Contents

INTRODUCTION...................................................................................................................................10

1.1 BACKGROUND................................................................................................................................10

1.2 MOTIVATION..................................................................................................................................13

1.3 RESEARCH AIM..............................................................................................................................13

1.4 SCOPE............................................................................................................................................14

1.5 THESIS ORGANIZATION..................................................................................................................14

TRADITIONAL LINK STRUCTURE-BASED RANK ALGORITHMS........................................15

2.1 OBJECTIVE AND USAGE OF HYPERLINK EXISTING IN WEB...........................................................15

2.2 PAGERANK ALGORITHM................................................................................................................16

2.2.1 Simplified PageRank Algorithm............................................................................................182.2.2 Improved PageRank Algorithm.............................................................................................19

2.3 HITS ALGORITHM.........................................................................................................................21

2.3.1 Analysis of HITS Algorithm...................................................................................................222.3.2 Analysis of HITS Link............................................................................................................23

3.4 SUMMARY......................................................................................................................................25

LITERATURE REVIEW......................................................................................................................26

3.1 RESEARCH ON TRADITIONAL RANK ALGORITHMS OF SEARCH ENGINES.....................................26

3.1.1 Problems and Improvements of PageRank Algorithm..........................................................263.1.2 Problems and Improvements of Hits Algorithm....................................................................30

3.2 TRADITIONAL DOMAIN ONTOLOGY-BASED CONCEPT SEMANTIC SIMILARITY COMPUTATION.....33

3.2.1 Ontology................................................................................................................................333.2.2 Three Main Semantic Similarity Computation Models.........................................................34

3.3 SUMMARY......................................................................................................................................34

METHODOLOGY................................................................................................................................36

4.1 RESEARCH QUESTIONS..................................................................................................................36

4.2 RESEARCH STRATEGY....................................................................................................................36

4.3 EVALUATION TOOLS......................................................................................................................39

4.3.1 The Ontology Tool.................................................................................................................39

IMPROVE CONCEPT SEMANTIC SIMILARITY COMPUTATION MODEL..........................40

2

5.1 DISCUSSION ON TRADITIONAL COMPUTATION MODELS...............................................................40

5.1.1 Distance-based Semantic Similarity Computation Model....................................................405.1.2 Content-based Semantic Similarity Computation Model......................................................415.1.3 Attribute-based Semantic Similarity Computation Model....................................................41

5.2 DECISION FACTS OF SEMANTIC SIMILARITY COMPUTATION.........................................................42

5.2.1 Directed Edge Category........................................................................................................435.2.2 Directed Edge Depth.............................................................................................................445.2.3 Directed Edge Density...........................................................................................................455.2.4 Directed Edge Strength.........................................................................................................455.2.5 Concept Node Attribute of the Two Side of Directed Edge...................................................46

5.3 ESTABLISHMENT OF IMPROVED COMPUTATION MODEL................................................................46

5.4 EVALUATION OF IMPROVED COMPUTATION MODEL......................................................................48

5.5 SUMMARY......................................................................................................................................50

IMPROVE RANK ALGORITHM BASED ON CATEGORIZATION TECHNOLOGY.............51

6.1 COMBINATION OF CATEGORIZATION TECHNOLOGY AND LINK STRUCTURE BASED ALGORITHM.51

6.2 BASIC IDEA OF CATEGORIZATION..................................................................................................54

6.2.1 Implementation of Categorization.........................................................................................546.2.2 Pre-categorization Processes................................................................................................55

6.2.2.1 Pre-categorization of Web Pages............................................................................................55

6.2.2.2 Pre-categorization of Keywords...............................................................................................55

6.3 MODELING.....................................................................................................................................58

6.3.1 Category Selective Mechanism.............................................................................................586.3.2 Integrating HITS Algorithm with Categorization.................................................................59

6.4 EVALUATION OF INTEGRATED ALGORITHM...................................................................................61

6.4 SUMMARY......................................................................................................................................62

CONCLUSION......................................................................................................................................63

7.1 SUMMARY OF CONTRIBUTIONS......................................................................................................63

7.2 FUTURE WORK...............................................................................................................................64

REFERENCES.......................................................................................................................................65

SEMANTIC RELATIONS....................................................................................................................68

“Linear Structure” Ontology....................................................................................................................71

3

List of Figures

Figure 1 Directed link graph ...........................................................................17

Figure 2 Simplified PageRank Algorithm...........................................................19

Figure 3 Improved PageRank Algorithm............................................................21

Figure 4 HITS algorithm on six-nodes graph......................................................24

Figure 5 Function image of ............................................................................33

Figure 6 Research strategy framework................................................................38

Figure 7 Main interface of Protégé 3.4................................................................39

Figure 8 Ontology of linear structure..................................................................49

Figure 9 Screenshot of Linear Structure..............................................................49

Figure 10 Pre-categorization framework.............................................................56

Figure 11 Relation between authority value and hub value................................58

4

List of Tables

Table 1 PageRank of each node in Figure 1........................................................21Table 2 Semantic relations...................................................................................43

Table 3 Experimental result.................................................................................49

5

Abstract

The appearance and rapid development of Internet has greatly changed the

environment of information retrieval. However, the rank algorithms for search engine

based on Internet are directly related to experiences in using when users perform

information retrievals in the new environment.

The existing rank algorithms for search engine are mainly based on the link structure

of web pages, and the two main representative algorithms are PageRank algorithm

and HITS algorithm. Many scholars and research institutions have made new

explorations and improvements based on these two algorithms, and some mature

integrated rank models suitable for search engines were generated.

In this paper, we study the shortcomings of search engines, and provide further

analysis on PageRank algorithm and Hits algorithm. Beside, we discuss the existing

improved algorithms based on link structure, and provide analysis on the

improvement ideas of existing search engine rank technology. Moreover, research on

traditional concept semantic similarity computation models based on domain ontology

is given as well.

According to the characteristics and shortcomings of existing models and algorithms,

we firstly propose an improved concept semantic similarity computation model. Then,

an improved rank algorithm which integrating categorization technology and

traditional link analysis algorithm based on it is given in this paper, which improves

HITS algorithm in two aspects, the pre-processing of Web pages and analysis on the

link structure of Web page. At last, the evaluations are provided as well.

6

Declaration

I declare that:

this thesis presents work carried out by myself and does not incorporate without

acknowledgment any material previously submitted for a degree or diploma in any

university;

to the best of my knowledge it does not contain any materials previously published

or written by another person except where due reference is made in the text; and all

substantive contributions by others to the work presented, including jointly authored

publications, is clearly acknowledged.

Qiaowei Dai

1st June 2009

7

Acknowledgements

I wish to express my sincere gratitude to my master thesis supervisor Dr. Jiuyong Li,

who is a Lecturer in the School of Computer and Information Science, for his helpful

suggestions, unreserved support, and encouragement throughout the research and

writing of this thesis. Besides this, I would also like to thank my course coordinator,

Dr. Stewart Von Itzstein for his encouragement and support. Last but not the least I

would like to express deepest thanks to my family for giving me the courage and their

support to study in Adelaide.

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CHAPTER 1

Introduction

1.1 Background

Search engines have gradually become a high efficient and convenient way for data

query and information acquisition to people. With the continuous development of

search engine technology, the current mature commercial search engines have

experienced several generations of evolution. Meanwhile, Web information retrieval

technology, which is the essence of search engines, including commercial products

has come out for about 20 years. In this period of time, great progresses in the aspects

of retrieval key technology, system structure design, query algorithm and etc. are

made, and a lot of commercial search engine services are being used on Web.

Compare with these progresses, the rapid increment of data on Web weakens the

achievement obtained in the research field of Web search in some degree, the massive

data quantity and frequent update speed have brought a completely new challenge as

well. Currently, the shortcomings existing in Web information retrieval are mainly

shown in the following aspects according to my research:

Low query quality

Low query quality is shown as when returning large amount of result pages,

however, the amount that really accords to users’ requirement is low. Moreover,

most of these relevant links don’t appear on the top of query results. Users have to

keep trying and turning pages in order to find valuable information, thus a lot of

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time is consumed by this process. In the age that Web information amount is

increasing continuously, this problem has become particularly outstanding.

Improving Web query quality is the most critical subject of current intelligent

information retrieval research, after Web mining technology is integrated, the

query quality of search engines can obtain great improvement.

Low query update speed

There are two reasons causing the low update speed of Web query results, one is

the low efficiency of the Crawler system of search engines, which the collection

period of documents is too long, after the index is completed, difference has

emerged between acquired content and the newest pages; the other one is the

update speed of Web documents has become faster and faster. Currently, many

Websites include dynamic pages, which are activated by the background database,

thus the change of database will directly cause these dynamic pages to be

changed. The update speed of part of static pages is increasing as well. When

many Web pages are continuously visited by Web Crawler by two times, the

change times of them will much higher than two times in the interval, so users

can’t obtain the content of these changes through query.

Lack of effective information categorization

Currently, most of the query results of search engines are provided in the way of

list and paging, all the relevant and irrelevant links are put together without

association, which is quite inconvenient for users with explicit query objective,

because they have to keep jumping or selecting between various links.

Categorizing and clustering query pages is an effective way to improve the quality

of user navigation, which can make users select some category quickly and

ulteriorly refine query targets in this category. For example, if we input “mining”

into Vivisimo, several categories such as “data mining”, “gold” and “Mining and

Metallurgy” will emerge, and users can make further query in every category.

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Keyword-based Web query lacks understanding of user behavior

In the view of the development of Web retrieval technology, keyword-based query

will be the most important retrieval way in a quite long period from now on.

Keyword-based query is a complicated retrieval mechanism implemented by the

Boolean combination of keywords. However, the query functions provided by

current search engines are quite limited, which only the most basic Boolean

connections between keywords are provided by most search engines. For instance,

Yahoo only provides two logical operators, which are “AND” and “OR”, and

compulsory applies one logical operator to all keywords. In many cases, it is quite

difficult to construct an effective query combination.

On the other hand, even to the same keywords, the search objective of different

users maybe different, it is closely related to the facts such as users’ personal

preference, the environment of context of current search, the previous search

history and so on. After these parameters are fully considered, a search engine that

accords to users’ requirement can be designed based on it. In Lawrence and Lee

Guile’s (1998) paper, they proposed a context environment-based Web retrieval

and query correction method.

Low index coverage rate of Web search engines

Currently, the coverage rate to Web of search engines is low than 50%, it is quite

difficult to completely index the whole Web because of resource restriction. In the

condition that the index coverage rate is low, when collecting documents, many

search services adopt same download priority for each page, which causes there

are many pages with low reference value remaining in index database, but some

relatively important pages are not indexed. In order to solve this problem,

discrimination of resource quality is needed in the process of Crawler traversing.

The pages with high quality should be downloaded in priority, and the index

database is constructed according to priority. In Chakrabarti, van den Berg and

Dom’s (1999) paper, they proposed an algorithm that analyzes Web document

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quality in real time and determine download priority by means of focus crawling,

which makes up the shortcoming of low coverage rate in some degree.

1.2 Motivation

According to Intelligent Surfer Model, we can consider that the user behaviors in

browning Web page are not absolute random or blind, but related to topic. That is to

the numerous outbound links of each Web page, the outbound links which belong to

the same or similar Web page category will have the higher click rate.

No matter PageRank algorithm or HITS algorithm, they objectively describe the

essential characteristics between Web pages, but rarely consider about the topic

relativity of users’ surfer habit. Link structure-based algorithm can be integrated with

other technology very well in order to improve the algorithm adaptability.

Categorization technology can simulate user subject-related habit, so as to improve

this kind of link structure-based algorithm. Categorization technology overcomes the

unreliability brought by the assumption that the users’ behaviour of visiting Web

pages is absolute random, and distinguishes the direction relation between Web pages

according to category attributes, thus categorization technology can be regarded as an

important supplementary to traditional algorithms.

1.3 Research Aim

My research aim is to establish an improved rank algorithm for search engines based

on domain ontology and categorization technology in order to make rank algorithm

simulate the actual user behaviors in browsing Web pages more accurate. To achieve

this research aim, we have three objectives.

The first objective is to analyze the traditional link　structure-based rank algorithms

for search engines in order to gain an insight into their principles and further studies.

Thought the research and analysis on traditional domain ontology-based concept

12

semantic similarity computation, we can gain a full understanding of the principles

and weaknesses of three common computation models. Therefore, our second

objective is to analyze and improve the decision facts of concept semantic similarity

computation, and then develop an improved concept semantic similarity computation

model in order to determine the relation between two categories in the categorization

process.

The third objective is, according to the study on category-integrated PageRank

algorithm, we firstly perform a pre-categorization process to Web pages and keywords

based on the improved concept semantic similarity computation model, and then

develop a category-based HITS algorithm to satisfy the final aim of this thesis.

1.4 Scope

This thesis will focus on researching the rank algorithm for search engines, which

needs to be able to adapt the link structure of network, and gives accurate feedback to

the information queried by user. A good rank algorithm should be able to filter the

content of Web pages, reject irrelevant Web pages, and displays the Web pages which

are most relevant and close to query condition to the top of the list. Meanwhile, the

waiting time of this kind of rank computation should be in user’s acceptable scope.

1.5 Thesis Organization

The thesis is structured as follow:

1 Chapter 2 (Traditional Link Structure-based Rank Algorithms)

2 Chapter 3 (Literature Review)

3 Chapter 4 (Methodology)

4 Chapter 5 (Improve Concept Semantic Similarity Computation Model)

5 Chapter 6 (Improve Rank Algorithm Based on Categorization Technology)

6 Chapter 7 (Conclusion)

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CHAPTER 2

Traditional Link Structure-based Rank Algorithms

Hyperlink is a very important component of Web. Through the hyperlink in a page,

users can arbitrarily link from a page of any WWW server in the world to the page of

another WWW server. Hyperlink not only provides convenient information

navigation, but also is an information organization method, which includes help

information that is very rich and effective to Web information retrieval.

2.1 Objective and Usage of Hyperlink Existing in Web

In order to convenience users in intra-Website navigation, the internal hyperlinks of a

Web page can convenience users to jump between different Web pages freely, so as to

avoid to use the “back” button of Web browser. A well-designed Website should be

able to jump from an arbitrary page of the Website to the other pages of the Website

by multiple links. The main function of this kind of hyperlink is to assist users to

orderly visit the whole Website content.

Another kind of hyperlink is extra-Website hyperlink, which is the most important

hyperlink form in Web hyperlink mining research. Generally, extra-Website hyperlink

always represents the page creator’s attention and preference of some Website or

content, or say, some potential relations exist. For example, adding the hyperlink of

Yahoo to a page represents the author’s recommendation and preference of Yahoo;

adding the hyperlink of Kdnuggets, which is a famous data mining Website, to a page

represents that the page author is interested in data mining, as well as the page itself is

14

possibly related to data mining research. If the URL of some page is linked many

times in Web, it indicates that the quality of its content is high; on the contrary, the

important degree is lower. This kind of evaluation mechanism is similar to the

reference in scientific paper, the importance of the paper with more times being

referenced by other people is higher than it with less times being referenced. In Web

retrieval, besides the times being linked by other documents, the quality of source link

document is also a reference factor of evaluating the quality of linked documents,

which the document linked or recommended by the high-quality document always has

higher authority. Web can be considered as a graph structure in hyperlink analysis,

and analyze the link relations between nodes can help solve the difficult problem that

text content-based retrieval can’t achieve content quality evaluation.

Compare with the traditional search engines which use the rank algorithms based on

the query results of word frequency statistics, the advantage of hyperlink analysis-

based algorithm is that it provides an objective and cheat-proofing (Some Web

documents cheat traditional search engines by adding invisible strings) Web resource

evaluation method. Currently, link analysis algorithm is used in many Web

information acquisition fields, including rank search engine document, search related

document, arrange priority order of Web Crawler’s URL crawling, etc (Dean, J &

Henzinger, RM 1999). Recently, compared with the word frequency statistics-based

method used by traditional search engines, the Web retrieval algorithms based on

hyperlink analysis such as PageRank algorithm has great improvement in the aspect

of improving retrieval precious (Haveliwala, HT 1999).

2.2 PageRank Algorithm

PageRank is a global link analysis algorithm proposed by S. Brin and L. Page (Brin, S

& Page, L 1998). It performs statistics to the URL link condition of whole Web, and

calculates a weight, which is called as the PageRank value of this page, to every URL

according to the factors such as link times, etc. This PageRank value is fixed, not

15

changeable with the change of query keyword, which is different from the local link

analysis algorithm HITS.

Figure 1 Directed link graph For example, in Figure 1, page includes a hyperlink referring to page , there exists

. Here, the hyperlinks between pages compose a directed graph . To a

node composing directed graph in every page, if and only if when includes the

hyperlink referring to page , there exists directed edge from to .

To node , nodes , , have contributions to the weight value of , because these

three nodes all exist directed edges to . The more the directed edges referring to

some node, the higher the node (page) quality is. The main shortcoming of this kind

of algorithm is that only the link quantity is considered, which means all the links are

equivalent, but whether the quality of source node itself is high or low is not

considered. In fact, the high-quality page in Web always includes high-quality links,

to the effect of linked document quality evaluation, the impact of the quality of source

node is always high than the quantity. For example, the links appearing in Yahoo

always have certain reference value, because Yahoo itself is a relatively authoritative

Website, just as the papers issued in top publications always have higher academic

value.

PageRank algorithm is in recursive form, its value relies on the linked times and the

w

b

a

u

vc

16

PageRank value of source link (Brin, S & Page, L 1998).

2.2.1 Simplified PageRank Algorithm

Simplified PageRank algorithm implements the basic recursive procedure of link

times and source PageRank. Let the pages on Web as , , …, , is the amount

of the extra-Website links of page , is the page set referring to page . Assume

Web is a strong connected graph (actually it is impossible, this problem will be

discussed in the next section), then the PageRank value of page can be expressed by:

The expression above can be written as , is the vector of , the arbitrary

element in matrix , which . If page refers to , then . Thus, vector

is the eigenvector of matrix . Because Web is assumed to be strong connected,

the eigenvalue of is .

From the definition above, we can find that PageRank is accord with Random Surfer

Model (Page, L, Brin, S & Motwani, R 1998). We can consider Random Surfer Model

in this way: Assume a user visits Web page by means of randomly clicking

hyperlinks, moreover, he doesn’t use “back” function and keeps continuous clicking.

The PageRank of page is essentially the probability of clicking page in the process

that a user browses the whole Web by means of random surfer. Motwani, R &

Raghavan, P (1995) had made further research on RSM, these works can be also used

to analyze the Web link attributes.

The computation of simplified PageRank algorithm can use iterative method, after

several times of iteration, stop the iterative procedure when the PageRank value

converge to the condition that deviation is small enough. For example, in Figure 2, the

computation procedure is shown below:

Select arbitrary random vector

17

7

8 If ( is the selected iterative threshold value), stop iteration. is the

PangeRank vector

9 , back to step 2

Figure 2 shows the computed rank value of every node in a small graph structure by

simplified PageRank algorithm. According to the RSM of PageRank, the sum of the

PageRank value of every node is .

Figure 2 Simplified PageRank Algorithm

2.2.2 Improved PageRank Algorithm

Simplified PageRank algorithm is only suitable for the ideal strong connected

environment, but in fact, Web is not a strong connected structure. Broder, A, Kumar,

R, & Maghoul, F’s (2000) paper shows there are only 28% pages on Web are strong

connected; 44% are one-way connected; and the remaining part forms Information

Isolated Island, which is neither linked by, nor links to other page. To simplify

PageRank algorithm, non strong connected Web exists two inextricable problems,

which are rank sink and rank leak. Rank sink refers to some local strong connected

Web graph doesn’t include the link referring to outside. Rank leak refers to the page

2

1 3

5 4

r2=0.286

r1=0.286 R3=0.143

r5=0.143 r4=0.143

18

that doesn’t include any external hyperlink. Actually, it is a special case of rank sink

when there is only one node in the strong connected graph. They will cause deviation

generating when analyzing graph structure. For example, if we discard the link from 5

to 1 in Figure 2, nodes 4 and 5 will form rank sink situation. If we use RSM to

simulate, we will fall into the dead circulation from 4 to 5 at last. Moreover, the rank

values of 1, 2 and 3 tend to 0, and the nodes 4 and 5 will share the rank, which the

total value is 1, of whole graph. If we remove 5 and its related links form figure 2,

node 4 will become a leak node. Because once this node is visited, the rank procedure

will stop here, thus, the rank values of all nodes will converge to 0. Therefore, Page

and Brin (Brin, S & Page, L 1998) proposed two methods, one is discarding all the

leak nodes which their outdegrees are 0, another one is introducing damping fact (

) in simplified PageRank algorithm. The appearance of makes PageRank

contribute to not only the node which it links to, but also the other pages on Web. The

expression of improved PageRank algorithm is shown below:

is the total node amount of Web subgraph that Web Crawler visits. As we can see

from the expression, the simplified PageRank algorithm is the special case when

.

Figure 3 shows the computed PageRank value of every node after removing the

hyperlink from 5 to 1 by improved algorithm. Every node has been adjusted by

parameter , which make their values all converge to a non 0 value.

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Figure 3 Improved PageRank AlgorithmFor example, in Figure 1, the PageRank value of each node is shown in the table

below ( ):

Table 1 PageRank of each node in Figure 1Node a Node b Node c Node u Node v Node w

PageRank 0.060210 0.071004 0.094177 0.047534 0.097881 0.125839

PageRank can use iterative algorithm to complete recursion. To the PageRank of each

node in Figure 1, about 15 times iterations are needed. Generally, in actual

computation, 100 times iterations are enough to converge (Haveliwala, H.T 1999).

PageRank algorithm is currently applied by Google search engine, which provides

high-quality Web retrieval service.

2.3 HITS Algorithm

HITS (Hypertext Induced Topic Search) algorithm is a kind of rank algorithm that

analyzes Web resource based on local link, which is proposed by Kleinberg in 1998

(Kleinberg, J 1999). The difference between PageRank and HITS is that HITS is

2

1 3

5 4

r2=0.142

r1=0.154 R3=0.101

r5=0.290 r4=0.313

20

related to query, and PageRank is a kind of query unrelated algorithm. As mentioned

in the section above, PageRank algorithm gives each page a rank value which is

unique and unrelated to query keyword, but HITS algorithm gives each page two

values, which are Authority value and Hub value.

Authority page and Hub page are two important concepts in HITS algorithm, which

are the concepts all related to query keyword. Authority page refers to some page that

is most related to query keyword and combination (Kleinberg, J 1999). For example,

when querying “University of South Australia”, then the homepage of UniSA, which

is http://www.unisa.edu.au/, is the page with the highest Authority value in this query,

and the Authority value of other pages theoretically should be lower than it. Hub page

is the page that includes multiple Authority pages (Kleinberg, J 1999). Hub page itself

may not have direct relation to query content, but through it, the Authority page with

direct relation can be linked. For example, when inputting the query combination such

as “Australian university”, the homepage of Australian Education Network, which is

http://www.australian-universities.com/, is a Hub page, which includes the links

referring to each university in Australia. Hub page can be used as the auxiliary

reference when computing Authority page, and itself can be returned to user as query

result (Chakrabarti, B, Dom, B & Raghavan, P 1998).

2.3.1 Analysis of HITS Algorithm

The central idea of HITS algorithm is that: Firstly, use text-based retrieval algorithm

to obtain a Web subset, and the pages in this subset all have relativity to user query.

Then, HITS performs link analysis on this subset, and find out the Authority pages

and Hub pages related to query in the subset. The selection of subnet in HITS

algorithm is acquired by means of keyword matching. This subnet is defined as root

set , then use link analysis to acquire set from root set , is the page that

includes Authority page and ultimately meet the query requirement. The process from

to is called “Neighborhood Expand”, and the algorithm procedure for computing

is shown below:

21

Use text keyword matching to acquire root set , which includes thousands of URL or more;10 Define to , that is and are equal;

11 To each page in , put the hyperlinks included by into set ; put the pages

referring to into set ;

12 is the acquired expanded neighbourhood set.

HITS algorithm needs three parameters, which are query keyword, maximum

capability of root set R, and maximum capability of expanded neighborhood . After

using the algorithm above, the pages in will have more Authority pages and Hub

pages which meet the query keyword.

2.3.2 Analysis of HITS Link

The process of HITS link analysis takes advantage of the attribute that Authority and

Hub are interacting to identity them from expanded set . Assume the pages in

expanded set are respectively 1, 2, …, n. represents the page set referring to

page , represents the page set referred by page . HITS generates an authority

value and Hub value for each page in . The initial value of computing initial

and can be an arbitrary value, similar to PageRank, HITS can use iterative method

to acquire convergence value. There are two steps in its iterative procedure, which are

step I and step O. In step I, the authority value of each page is the sum of the Hub

values of pages referring to it. In step O, the Hub value of each page is the sum of the

authority values of pages referring to it. That is:

I:

O:

The two steps, I and O, are based on the fact that one Authority page is always

referred by many Hub pages, and one Hub page includes many Authority pages. HITS

22

algorithm iteratively computes the two steps, I and O, till they converge. At last,

and are the Authority and Hub value of page . The procedure is shown below:

Initialize , ;

13 Iterate procedure I, O; Perform iteration I; Perform iteration O; Normalize the

value of and , let ;

14 Complete iteration

Figure 4 shows the application of HITS algorithm in a subgraph including 6 nodes.

Figure 4 HITS algorithm on six-nodes graphAs shown in Figure 4, the authority value of node 5 is equal to the Hub values of

nodes 1, 3 which refers to it, after normalization, the value is 0.816.

Assume is the matrix of subgraph, then the value of position in matrix is

equal to 1 (if page refers to ), or 0. Set to be the authority value vector

21 3

54 6

h=0a=0.408

h=0a=0.816

h=0a=0.408

h=0.408a=0

h=0.816a=0

h=0.408a=0

23

, to be the hub value vector , then the iteration I, O can

be expressed as , . After completing the iteration, the values of

authority and hub respectively satisfy , , which and are

constant in order to satisfy the normalization condition. Thus, vector and vector

respectively become the eigenvector of matrix and matrix . This feature is

similar to PageRank algorithm, their convergence speeds are decided by eigenvector.

3.4 Summary

PageRank and HITS are currently the representations of Web retrieval algorithm

based on hyperlink mining. Through the analysis of Web hyperlink relation, we can

greatly improve the accuracy of Web retrieval, and overcome the disadvantage based

on context matching method. Currently, many search engine begin to use similar

algorithm to improve the query precious, for example, Google uses PageRank, Toema

and Altavista also adopt similar technology.

On the other hand, there are defects existing in both PageRank and HITS. PageRank

is independent to query, thus its computation amount is relatively small, but will lose

part of performance on content matching. Although HITS is query related, its link

analysis is only limited in the Web subgraph with thousands of nodes. It can’t reflect

the link condition of whole Web.

24

CHAPTER 3

Literature Review

3.1 Research on Traditional Rank Algorithms of Search

Engines

3.1.1 Problems and Improvements of PageRank Algorithm

The PageRank algorithm is more concerned about old pages, because the probability

of old pages being linked by other pages is much higher, but in fact that new pages

may contain information with better values.

Another problem it may cause is called ‘topic drift phenomenon”. The following

condition should be considered: The portal Websites on Web are always inclined to

make a clean sweep of all aspects of information, which presents as there exists

Website hyperlinks of various topics on their homepages. Meanwhile, many pages

will regard them as a guide for their further information reference, and then include

them in their own links. When searching some key words, if these portal Websites are

in the scope of consideration, there is no doubt that they will acquire the highest

authority, thereupon topic drift phenomenon generates. These portal Websites can be

always found on the top of the searching results, although they also contain the

information required by users, but usually the contents they contain are much

generalized than what the users expect, which is far from satisfactory. In contrast,

25

some professional Websites are more authoritative in describing these topics.

PageRank algorithm is not able to distinguish the hyperlinks in page being related to

its topic or not, that is to say, it is not able to judge the similarity of page content.

Thus, it is easy to cause topic drift problem, for example, Google and Yahoo are the

most popular Web pages on the Internet, and they have very high PageRank values.

Thus, when users input a query keyword, these Web pages will often appear in the

result set of this query, and occupy the very front positions. In fact, sometimes this

Web page is not even related to the users’ query topic.

In Kleinberg’s Hits algorithm paper (1999), he explicitly pointed that those links that

link back to the same Website cannot be counted in Web graph, instead, they should

be discarded. They are a kind of nepotistic links, obviously, not containing any

authority information. After the publication of Kleinberg’s paper, in Bharat and

Henzinger’s paper (1998), they pointed that there exists another nepotistic link, which

is the nepotistic link between two different Website, and this kind of links are trending

to increase rapidly. Moreover, this kind of nepotistic link may be generated in the

construction of Websites accidently. For instance, all the sub Websites of Yahoo have

links referring to main Website. The nepotistic link between two Websites will make

their authorities keep increasing in the iterative process, either for PageRank

algorithm or Hits algorithm.

In order to solving the problem that PageRank algorithm concerns old pages too

much, Ling & Fanyuan (2004) proposed an accelerated evaluation algorithm. This

algorithm make the valuable contents on network deliver in a faster speed,

meanwhile, the evaluation value of some pages containing old data will drop in a

quicker speed. The core idea of this algorithm is to predict the expected value of one

certain URL in a period of further time by analyzing the change condition of

PageRank value based on the time series, and regard it as the effective parameters of

retieval service provided by search engine. This algorithm defines a URL accelerated

26

factor , which is given by:

where is the document amount of the whole page set. The expression of

accelerated PageRank is:

where is the value of URL in the latest time, is the slope of the quadratic

fitting curve of the PageRank value of this URL in a period of time, is the day

interval from the time that the page being downloaded in the latest time, and is

the amount of the documents in the document set downloaded in the latest time. When

users retrieve, search engines will decide the URL position in the retieval results

according to the predicted PageRank value.

The WebGather search engine (Ming, L, Jianyong, W & Baojue, C 2001) developed

by Beijing University applied another way to overcome this weakness, which is to

give compensation to new Web pages. The clicking amount of linked LHN Web pages

can be divided into same Website link amount and different Website link amount.

Different Website link is called pure LHN, and gross LHN contains both. Only pure

LHN is considered here.

To new Web pages, they are not linked by other pages yet, so compensation is given.

27

where is the current time, is the compensated limit time, and is the

time when Web pages are published.

After compensation weight being introduced, the new link weight is:

After standardization:

Haveliwala (2002) proposed a topic-sensitive PageRank algorithm in order to solving

the topic drift phenomenon. This algorithm considers that some pages are thought to

be important in some field, but it doesn’t represent that they are also important in

other fields. Therefore, the algorithm firstly lists 16 basic topic vectors according to

Open Directory (The Open Directory Project, which is a Web Directory for over 2.5

Million URLs), and then to every Web page, computing the PageRank values of these

basic topic vectors in offline condition. When users queries, according to the query

topic or query context inputted by users, the algorithm computes the similarity

between this topic and known basic topic, and chooses a closest topic from basic topic

set to replace the users’ query topic. The formal expression of the algorithm is shown

as follow:

where is the topic-sensitive vector of Web page . This algorithm can effectively

avoid some obvious topic drift phenomenon, for example, when querying “jaguar”, if

the instruction of context is available, the algorithm can explicitly distinguish

whatever users tried to search:

28

1 jaguar car; 2 jaguar football team;

3 jaguar product;

4 jaguar, which is a kind of mammal,

thus, provide high-quality recommendation result set.

3.1.2 Problems and Improvements of Hits Algorithm

Multiple Websites posses some links that recursively refer to each other due to some

reason, which causes “faction attacks” emerge between these Websites. For instance,

some enterprise Websites are designed by the same Website design company for

different companies, it should be possible that there are friendly links between them.

The impact brought by faction attacks is similar to nepotistic links, but it is more

difficult to be detected than nepotistic links, because larger scope of Web graph needs

to be inspected. Another type of problem is called “mixed hub page phenomenon”,

which is a hub page simultaneously possesses links referring to several categories of

completely different topics. For example, a hub page related to a movie awards

usually includes a lot of links referring to movie companies. Mixed hub page is more

difficult to be detected by computers than faction attacks, furthermore, the probability

that it emerges is higher as well. It is possible for mixed hub page to mix the Web

pages with different topics together, especially in HITS algorithm, it is quite easy to

involve Web pages that are irrelevant to current topic in the process of constructing

extended set, and due to these Web pages have a large amount of links refer to pages

with higher authority, they cannot be discarded from results. In Google’s PageRank

algorithm, this impact can be reduced by adjusting the random surfer probability .

In Hits algorithm, firstly a basic set is constructed, and then extended to extended set

through basic set, finally the whole Web graph is formed. The reason of doing this is

possibly that the result acquired by the information retrieval system in the first step

doesn’t include the pages that users really demand. For instance, when querying with

keyword “browser”, the pages returned by information retrieval system usually

29

doesn’t contain the pages of Netscape Navigator, Microsoft Internet Explorer, because

their pages will usually avoid using words such as “browser” to make product

promotion. Furthermore, usually some personal page will use words such as “best

viewed with a frames-capable browser…”, which causes the originally important

Netscape and Microsoft’s pages cannot be included in the results in the first step. This

problem can be solved by expended set, because the required Web pages can be

acquired through hub page. Due to this characteristic, HITS algorithm can be

impacted by the nepotistic links, faction attacks and mixed hub page mentioned

above. When constructing extended set, too many pages irrelevant to topic are

involved, and they are also with higher authority because of possessing links referring

to each other. If we restrict the radius when the extended set is constructed, it is

possibly that we can’t get enough pages. The really decent pages can be acquired only

if the radius is big enough when constructing extended set, but by then too many

irrelevant pages have been involved and causing “topic pollution phenomenon”.

Besides, similar to PageRank algorithm, Hits algorithm is also impacted by “topic

drift”. After including these portal Websites through extended set, Hits algorithm will

face the same difficulty as PageRank.

Bharat and Henzinger (1998) improved the computation method of authority weight

and hub weight by means of introducing relevance weight to hyperlinks, if the

relevance weight of hyperlink is smaller than certain threshold, then we consider the

impact of this hyperlink to page weight can be neglected, and this hyperlink will be

discarded from the subgraph. Besides, Chakrabarti, Dom and Gibson (1999) proposed

an idea that split big hub page into smaller units. A page always includes many links,

which possibly not relevant to the same topic. In this situation, in order to get a good

result, it is better to divide the hub page into continuous subsets and then make a

process, these subsets are called pagelet. The single pagelet refers to a topic more

concentrative than the whole hub page, so better retrieval results can be acquired by

computing weight for every pagelet. In the Clever system which is an application

30

example of HITS algorithm, the author computed the weight of hyperlink by means of

matching query keyword with the text around hyperlink and computing the word

frequency, and then replace the corresponding value in adjacency matrix with the

computed weight, thus achieves the objective of introducing semantic information

(Chakrabarti, B, Dom, B & Raghavan, P 1998).

Time parameter is introduced to improve HITS algorithm as well. To the reference of

a certain determined Web page, i.e., node reference node , its application time, to

a great extend, reflects whether this referenced node is authoritative or not. In reality,

the visiting time of the authority pages which the users really want to visit should be

relatively long, and to those visits act as navigations occasionally or for some other

purposes, the visiting time is relatively short. In other words, if users’ visiting time to

a certain page is relatively long, then we can consider this page as the page that the

users want to visit, which is target page. If this information is applied in the

computation of authority weight in HITS algorithm, the accuracy of HITS algorithm

can be greatly improved.

Xuelong, Xuemei and Xiangwei (2006) proposed a time parameter control model

which is described as follows: Define the hyperlink weight related to keyword

which refers from page to page is , this final value of is

determined by three facts: the link referring from to ; the emergence times of

query keyword in the hyperlink characters, which is ; the visiting time that visits

, which is . In order to control the result more precisely, a coefficient is introduced

to control the proportion, which is in hyperlink weight, of semantic information of the

surrounding characters in , and parameter is introduced to control the impact

of visiting time to weight, then the weight control model with time parameter is given

by:

where can be adjusted according to different page sets, and the value of will

31

continuously increase in the iterative procedure of computing authority weight, but we

only concern about the relative value between them, not the absolute value.

reflects the non linear increment of its authority weight with the increment of visiting

time, and other function can be constructed to control the proportion of visiting time

in weight as well, the function above is the simplest form.

Figure 5 Function image of

3.2 Traditional Domain Ontology-based Concept Semantic

Similarity Computation

3.2.1 Ontology

Ontology is a terminology in philosophy in the earliest, which is a systematic and

comprehensive explanation to objective existence, and its core is to represent the

abstract essence of objective reality (Zhihong, D & Shiwei, T 2002). In recent years,

ontology research is becoming mature, but in various literatures, the definition of

ontology and usage of related terminology are not completely consistent. Neches et al.

(1991) introduced the concept of ontology into artificial intelligence, and gave the

earliest definition about ontology, which is that the relation between basic

terminologies constituted by related domain knowledge and terms, as well as the

extension rules determined by these basic terms and relations. Gruber (1993) gave the

t

tTime

32

most popular definition of ontology, which is that ontology is the definite rule

explanation of concept model. Later, Studer, Benjanmins and Fensel (1998) made

further research on ontology, and gave the most complete definition about ontology,

which is that ontology is a definite formal standard specification sharing concept.

There are four level meanings included here, which are concept model, explicit,

formalization and common sharing (BernersLee, T, Hendler, J, Lassila, O 2001).

3.2.2 Three Main Semantic Similarity Computation Models

Concept semantic similarity computation has wide application in the fields of

information retrieval, information recommendation and filtering, data mining and

machine translation, etc, which has become a hot point of current information

technology research (Sujian, L 2002). Currently, to the semantic similarity

computation between concepts, researches are performed mainly from three different

views. Leacock (2005) proposed a distance-based semantic similarity computation

model. This kind of computation model is simple and visual, but it extremely relies on

the ontology hierarchical network established in advance, and the network structure

directly influences semantic similarity computation. Lin (2000) proposed an

information content-based semantic similarity computation model. This kind of

computation model has more persuasion in theoretically, because when computing

concept semantic similarity, the related knowledge of information theory and

probability statistic theory are fully used. But this kind of computation model can only

grossly quantify the semantic similarity between concepts; it can’t distinguish each

concept semantic similarity more detailed. Tversky et al. (2004) proposed an attribute-

based semantic similarity computation model. This kind of model can simulate

people’s regular understanding and discrimination between things in real world, but

this method only considers a unique attribute fact, so to every attribute of objective

things, performing detailed and comprehensive description is required, which is quite

difficult.

33

3.3 Summary

In this chapter, we firstly analysis the problems of PageRank and HITS algorithm, and

some comparisons between them are made as well. Meanwhile, the ideas of several

improved methods of classical algorithms are given. Then, some basic information

about ontology and domain ontology-based concept similarity computation is

introduced, and the further research and analysis will be given in Chapter 5.

34

CHAPTER 4

Methodology

4.1 Research Questions

My main research objective is to improve rank algorithms in order to make them more

concerned about users’ surfer behaviors. To achieve this target and make my research

easier, some research questions are listed as follows:

What facts will influence the relation between two concept nodes in the

hierarchical network structure in domain ontology?

A web page maybe related to several topics, how to determine the category it

belongs to. If it is categorized into a certain category, how to make the other

categories it related to being considered as well.

How to implement categorization to Web pages and keywords?

Because the amount of Web pages and keywords are huge, do I need to introduce

any mechanism to reduce the unnecessary amount?

4.2 Research Strategy

The whole process of my research strategy is shown in Figure 6, which can be divided

in to two main steps, the first step is to model an improved domain ontology-based

concept similarity computation model, and the second step is to integrate rank

algorithm with categorization technology.

35

In the first step, firstly we will discuss the traditional three domain ontology-based

concept similarity computation models in order to get a full understanding about their

ideas, computing processes, advantages and disadvantages. Then, we will discuss and

improve the decision facts that have impact on directed edge weight in ontology

hierarchical network. At last, the improved domain ontology-based concept similarity

computation model including five decision facts will be modeled and evaluated.

In the second step, firstly we will discuss an existing categorization-integrated rank

algorithm, which combines PageRank with categorization technology in order to

provide a theoretical support. Secondly, the basic idea of categorization in this paper

is given, which will describe how categorization is implemented and the processes of

pre-categorization based on the improved model constructed in step one. Then, a

screen mechanism is introduced to filter the massive data amount. At last, the

improvement and evaluation of HITS algorithm integrated with categorization will be

provided.

36

Figure 6 Research strategy framework

Distance-based

Content-based

Attribute-based

DepthCategory Density Strength AttributeModeling Evaluate

Research on combination of PageRank and categorization

Define the basic idea of categorization Modeling Evaluate

How to implement categorization

Pre-categorization of Web pages

Pre-categorization of keywords Categorization

similarity table

Screen mechanismCombine HITS with categorization

Research on traditional domain ontology-based concept semantic similarity computation models

Improvement of decision facts

Modeling and evaluating improved domain ontology-based concept semantic similarity computation model

Constructing categorization similarity table according to the improved model

Performing pre-categorization processes according to categorization similarity table

Modeling and evaluating HITS algorithm integrated with categorization

37

4.3 Evaluation Tools

4.3.1 The Ontology Tool

The ontology tool adopted to evaluate the improved concept semantic similarity

computation model in this paper is Protégé 3.4, which is an ontology modeling tool.

Protégé is designed by Stanford University to edit instance and acquire knowledge,

which is currently the most popular ontology development tool. It shields the

shortcomings of many current ontology creation languages, and provides a friendly

GUI interface which is shown in Figure 7, which makes it much easier to edit class,

instance and attribute.

Figure 7 Main interface of Protégé 3.4

38

CHAPTER 5

Improve Concept Semantic Similarity Computation Model

5.1 Discussion on Traditional Computation Models

5.1.1 Distance-based Semantic Similarity Computation Model

The basic idea of this computation model is to quantify the semantic distance between

concepts by using the geometric distance of two concepts in hierarchical network

(Qun, L & Sujian, L 2002). The easiest computation method is to consider the

distances of all directed edges in network as equal importance, denoted by 1. Thus,

the distance between two concepts is equal to the amount of directed edges

constituting shortest distance in hierarchical network of the node which these two

concepts corresponds to. According to this idea, a simple semantic similarity

computation model can be obtained:

where is the maximum depth of network structure, is the amount of

directed edges of the shortest path between concept node and .

However, the above computation model is very rough in computing the semantic

similarity between concepts, which the difference between directed edges in network

structure is not considered. Then, Leacock (2005) performed an improvement to the

computation model based on it, and proposed an improved distance-based semantic

similarity computation model:

where is the closest common ancestor node of concept nodes and

39

in hierarchical network, is the shortest distance of concept nodes

and in hierarchical network, and is the maximum depth of network.

5.1.2 Content-based Semantic Similarity Computation Model

The basic principle of content-based semantic similarity computation model is that if

the more information two concepts share, the higher semantic similarity between

them; contrarily, the less information two concepts share, the lower semantic

similarity between them (Xiaofeng, Z, Xinting, T & Yongsheng, Z 2006). In

hierarchical network, every concept can be considered to be the refinement to its

ancestor node, so it can be nearly interpreted as every child node includes the

information contents of its entire ancestor node. Thus, the semantic similarity of two

concepts can be measured by the information contents of their closest common

ancestor node.

According to information theory, if the higher frequency a concept appears, the less

information amount it includes; contrarily, the lower frequency a concept appears, the

more information amount it includes. In hierarchical network, the computation

formula for quantifying the information amount of every concept node is:

where is the probability that concept appears in training material, is

the information amount that concept has.

Thus, according to the above quantization formula of concept information, the

semantic similarity computation model between arbitrary two concepts in hierarchical

network can be obtained.

where is the closest common ancestor node of concept nodes and

in hierarchical network.

5.1.3 Attribute-based Semantic Similarity Computation Model

In real world, the process that people distinguish and associate different things

generally by means of comparing the inherent attributes between things (Qianhong, P

& Ju, W 1999). If two things have many same attributes, it indicates that these two

thins are very similar; contrarily, it is opposite. Thus, the basic principle of attribute-

40

based semantic similarity computation model is to judge the similarity degree of

attribute set which the two concepts corresponding to. Tversky proposed an attribute-

based method for computing concept semantic similarity:

which is the attribute set that concepts and commonly posses,

is the attribute set that concept possesses but concept doesn’t possess,

is the attribute set that concept possesses but concept doesn’t possess.

Besides, L. Rips proposed a multi-dimensional attribute-based semantic similarity

computation model: Set concept and respectively has attributes, and the

attribute value respectively is ,

.

where is adjustment factor.

5.2 Decision Facts of Semantic Similarity Computation

In a directed no-loop hierarchical network constituted by domain ontology, the

weights of directed edge may be different, that is to say the semantic similarity

between parent node and child node located in the two ends of different directed edge

is different. Thus, it indicates that the influence of weight needs to be considered

when computing the distance length between concepts. According to my research,

there are five main facts influence the weight of directed edge in ontology hierarchical

network:

The category of directed edge between parent node and child node

The depth of directed edge constituted by parent node and child node in

hierarchical network

The density of parent node and child node in hierarchical network

The strength of directed edge constituted by parent node and child node in

hierarchical network

The attribute of concept node of the two ends of parent node and child node

41

5.2.1 Directed Edge Category

There are many categories of relations between concepts, which is shown in Table 2:Table 2 Semantic relations

Seq.

No.

Semantic

relation

Extraction rule

1 ISA If

Then

2 AKO If

Then

3 Have If

Then

4 Can If

Then

5 Is If

Then

6 Part-Of If

Then

7 Composed

-Of

If

Then

8 Belong-To If

Then

9 Time If

Then or

10 Position If

Then

11 OthersIf

Then

However, in the hierarchical network constituted by domain ontology, only three main

relations are generally considered, which are inheritance relation, entirety-part relation

and synonymous relation, because these three relations have the highest proportion.

42

The weights that different directed edge categories corresponding to are different. To

synonymous relation, its nodes of two ends represent the same meaning, so the weight

of this edge should be bigger than it of the other two categories. Besides, the directed

edge weight of inheritance relation is generally considered to be bigger than it of

entirety-part relation. Thus, the relations about directed edge weight and their

categories can be obtained:

where is the weight of directed edge constituted by child node and its

parent node .

5.2.2 Directed Edge Depth

Domain ontology can be considered as hierarchy network graph. There is only one

ingress node in this graph, which is the maximum concept of this domain. The

second-level nodes are the partition of ingress node (first-level node), and the third-

level nodes are the further refinement based on second-level nodes, and so on. Every

level is the concept refinement of the level above. The meanings of concept are

concrete in lower level; contrarily, the meanings of concept are abstract in higher

level. Thus, the weight of directed edge is related to its depth in hierarchical network,

so the relation about directed edge weight and its depth can be obtained:

where is the depth of node in hierarchical network.

5.2.3 Directed Edge Density

The overall density in domain ontology hierarchical network is a fixed value, but the

density in different place is different. If the node density of a certain local area in

hierarchical network is larger, it indicates that the refinement to concept is bigger

here, and the weight of corresponding edge is larger. Thus, the relation about directed

edge weight and its density can be obtained:

43

where and are the ingress degree of parent node and

child node in hierarchical network, and respectively

represents the egress degree of parent node and child node in hierarchical

network, and and represent the ingress degree and

egress degree of hierarchical network graph.

5.2.4 Directed Edge Strength

In the hierarchical network constituted by domain ontology, a parent node may have

multiple child nodes. If a child node is more important than the other nodes to this

domain, the weight of directed edge constituted by this child node and its parent node

should be bigger. Thus, if we use the condition proportion to quantify the strength of

directed edge, the following can be obtained:

where represents the former is important than the latter

and (In hierarchical network, the place that child node appears can

be nearly considered as parent node appearing as well.)

and (According to the computation model based on

information content.)

where represents the strength of directed edge constituted by child node and

parent node.

where is adjustment factor.

5.2.5 Concept Node Attribute of the Two Side of Directed Edge

Domain ontology hierarchical network not only makes correct definition to the

concepts and their relations in the domain, but also makes detailed description to the

attribute of every concept. Thus, if the concept that the child node and parent node of

the two side of directed edge corresponds to possesses more same attributes, it

44

indicates that the relation between parent node and child node is closer, and the

weight of directed edge constituted by them is larger. Thus, the relation about directed

edge weight and its attribute can be obtained:

where and is respectively the attribute set of concept and concept

, is the intersection attribute set of concept and concept ,

is the union attribute set of concept and concept , is the

amount of statistic attribute.

5.3 Establishment of Improved Computation Model

In this section, according to the special characteristic of domain ontology, we

establish an improved concept semantic similarity computation model by taking

advantage of the five influence facts about directed edge weight analysing in the

section 5.2. The procedure of establishment is described as follow:

1 The domain ontology completed by domain specialists can be considered as a hierarchical, directed and no-loop graph,

where is the set of all the nodes in graph, and each node represents the set of

concept and its attribute in domain, is the set of all the directed edges in graph,

and each directed edge represents some kind of relation existing between nodes.

2 As mentioned in section 5.2, the unit directed edge weight of hierarchical network

constituted by domain ontology is related to five facts, so, the facts influencing

weight need to be fully considered when qualifying the weight of directed edge.

Thus, the expression of directed edge weight should be:

After substituting the relations between directed edge weight and category, depth,

density, strength and attribute analysing in section 5.2, we can obtain:

where is adjustment factor, , .

45

3 Because the length of unit directed edge is inversely proportional to the weight of

directed edge, the computation model of unit directed edge length can be obtained:

where is adjustable factor.

4 As the computation formula of unit directed edge length in ontology hierarchical

network is known, so the distance between any two concept nodes in hierarchical

network can be obtained (Here, we still use Leacock computation model to

compute the distance between two concepts in domain ontology):

where is the closest common ancestor node of node and ,

is the set of all the nodes in the shortest path of node and in

hierarchical network.

5 As the distance between any two concepts in ontology hierarchical network is

known, the semantic similarity computation model of any two concepts can be

obtained:

where is amplification factor.

5.4 Evaluation of Improved Computation Model

Figure 8 shows part of the structure graph about the subject “Data Structure”, which is

constructed according to the construction rule of ontology, the numbers in the graph

represent the information amount of corresponding concepts.

Ontology modeling tool Protégé 3.4 is adopted to create part of the ontology of data

structure in the experiment. The similarity values between concept “Linear Structure”

and other concepts are obtained by means of the improved concept semantic similarity

computation model. When computing semantic similarity, generally the impact of the

facts such as attribute and class are stronger than density, strength and depth, so the

parameter values are: . Figure 9

shows the created “Linear Structure” ontology.

46

Figure 8 Ontology of linear structure

Figure 9 Screenshot of Linear Structure

Linear Structure Linear List

Stack

Sequential Stack

Linked Stack

Queue

Circular Queue

Linked Queue

Sequential storage structure

Linked storage structure

String

Static Allocation

Dynamic Allocation

Circular Linked List

Single Linked List

Double Linked List

Double Linked Circular List

7.03

9.02

8.92

8.41

8.5

8.61

9.03

9.31

9.81

9.5

9.61

10.46

11.46

13.46

10.36

12.46

10.86

47

Table 2 shows the data values between concept “Data Structure” and other several

concepts obtained by means of different similarity computation methods in the same

ontology structure. As we can see from the table, compare with the traditional

computation models, the improved computation model is much closer to the expert

experience in the field of quantifying the semantic similarity between concepts.

Table 3 Experimental result

Concept Improved

computation

model

Traditional computation

model

Expert

experience

Content

-based

Distance

-based

Sim(Linear structure, Linear List) 91.3% 62.9% 87.6% 90%

Sim(Linear structure, Stack) 90.5% 81.2% 83.2% 88%

Sim(Linear structure, String) 89.9% 81.1% 78.1% 85%

Sim(Linear structure, Queue) 84.2% 80.5% 78.3% 83%

Sim(Linear structure, Linked Stack) 80.3% 86.5% 87.6% 78%

Sim(Linear structure, Sequential Stack) 77.4% 63.3% 78.5% 77%

Sim(Linear structure, Sequential storage) 76.3% 80.1% 64.6% 75%

Sim(Linear structure, Linked storage) 68.2% 73.6% 62.8% 70%

Sim(Linear structure, Circular Queue) 67.8% 62.7% 70.3% 68%

Sim(Linear structure, Linked Queue) 65.3% 59.3% 63.6% 66%

5.5 Summary

In this chapter, we perform analysis and explanation on the three traditional semantic

similarity computation models, and propose an improved domain ontology-based

concept similarity computation model according to the advantages and disadvantages

of these three computation models as well as the specific properties of domain

ontology. In this computation model, we firstly perform effective quantization to the

five facts which have impacts on the directed edge weight of ontology hierarchical

network according to the characteristic of ontology network, then make linear

weighted combination according to the impacts of these five categories of facts on

directed edge weights, so as to quantify the semantic similarity between the concept

nodes in ontology network more comprehensively.

As shown by the experimental result, this improved computation model can reflect the

semantic similarity between concepts with better accuracy, which provides an

effective quantization for the semantic relations between concepts.

48

CHAPTER 6

Improve Rank Algorithm Based on Categorization Technology

The traditional rank algorithms for search engines are all based on the link structure

analysis of Web page. In traditional PageRank algorithm, the PageRank score

represents the probability that user browses a certain Web page, and the score of HITS

algorithm is based on the Authority and Hub of Web page. These algorithms are all

based on the assumption that the process of user browsing Web pages is absolute

random, but ignore the influence to link direction from the similarity of Web page

content, which makes them not able to fully reflect the difference in importance

degree of this Web page between different users. In this chapter, we design an

improved HITS algorithm based on categorization, which combines rank algorithm

with categorization technology.

6.1 Combination of Categorization Technology and Link Structure Based Algorithm

In previous research works, some categorization technology-based improved

algorithms are already proposed. For example, CategoryRank Algorithm (Weizhu, C,

Ying, C & Yan, W 2005) is a kind of integrated rank algorithm that combines

PageRank with categorization technology

To any link from to , CategoryRank algorithm obtains a weight according to the

category that and belong to and the similarity degree between categories, adds

this weight value to , so as to modify the computation method of

PageRank, and forms single link-based CategoryRank computation. Because and

belong to the same or similar categories, but and belong to different categories, so

the link from to is obviously important than it from to , which represents in

49

the wider width.

To each link referring from to , the algorithm will firstly use categorizer, which

regards the content of and as the input of categorizer. Assume in the

categorization results, trends to category more, and trends to category

more. Here, assume and respectively represents the probability

that belongs to category and , then, in CategoryRank algorithm, the similarity

between and can be expressed as:

where is the similarity degree between and , and respectively

represents the eigenvector of category and .

At last, normalization is need to in order to satisfy:

where the relation between and is , which there exists a link referring from

to . Make modification to PageRank formula, replace with category-related

value , then the computed value by means of link-based CategoryRank

algorithm is:

Besides considering the relation between Web pages, meanwhile, the different

appearances of single Web page in different categories need to be considered. For

example, a certain Web page is possibly not important in category , but very

important in category . Thus, according to different catefories, Web page-based

CategoryRank algorithm will compute out different importance weight value. Assume

a system includes categories in all, then each Web page will include category

weight values according to categories. At last, the system will generate

CategoryRank vectors according to different categories, which each vector is

corresponding to a category, and the vector element value is the category weight value

which each Web page belongs to in Web. Assume Web includes pages in all, then

all the computation results can be expressed by a matrix with elements,

50

which is the element value of matrix, representing the category weight of Web

page in category .

When creating the vector of category , several processes can be performed to .

For example, it can be combined with value which is used to compute

relativity, or PageRank value. Here, the algorithm directly combines into the link-

based CategoryRank computation formula mentioned above. We can consider that

when changing Category values by introducing category information into formula,

these values will eventually be transferred to rank score, so as to achieve the target

that introduce page categorization technology into ranking. Before modify the link-

based CategoryRank, the algorithm will firstly normalize in order to satisfy:

where is Web page ID, is the category ID, is the total Web page amount of in

whole Web.

At last, according to each category , a single CategoryRank vector need to be

created. Thus, the formula can be modified as :

The formula above considers not only the category similarity of links, but also the

category difference of Web pages. Finally, the category information of each Web page

is integrated into Category algorithm.

CategoryRank algorithm is based on the category information between two arbitrary

Web pages, and performs analysis and computation to link graph. Compared with

PageRank algorithm, this algorithm can simulate users’ habit in browsing Web pages

more accurate. Meanwhile, according to each Web page in Web, the algorithm

computes their category attributes, which directly reflects the importance degree of

this page to different users.

Compared with traditional RankRank algorithm, CategoryRank algorithm has added

category parameters and computation.

51

6.2 Basic Idea of Categorization

6.2.1 Implementation of Categorization

In order to implement categorization, we proposed two assumptions:

1 Every Web page is categorize-able, and can be marked with a main category, denoted by C.

2 There is relation at different degree between every category, expressed by

category similarity degree .

To every Web page, there is always a related content topic, which is subordinated to a

certain category of knowledge, or has a certain category of feature. These categories

can be also subdivided into more professional and specific subcategories. The

category division is according to the content topic of Web page, not the properties. For

example, the homepage of Google Website can be divided to the search engine

subclass of network technology; a news class can be divided to the related class of this

news topic.

To the same Web page, its topic may be related to category , as well as related to

category B and category C at different degree. This relativity can be divided into:

Directly topic-related

The content of a Web page may also include several categories of topic, so there is

a directly topic-related relation between this Web page and related topics. For

example, the homepage of msn includes many different aspects of class, so this

Web page is directly related to these topics. The direct relativity between a Web

page and each topic can be expressed by vector group

.

Indirectly topic-related

There are relations existing between categories. If Web page is determined to

be related to category , and the relativity between category and category is

very high, then can be considered to have indirect topic relativity to category

as well. Its relativity to is determined jointly by both the relativity between

category and category and the relativity between Web page and category .

The indirect relativity between a Web page and each topic can be expressed by

vector group .

The vector sum of direct and indirect topic category of Web page topic is the topic

category vector of Web page:

52

6.2.2 Pre-categorization Processes

6.2.2.1 Pre-categorization of Web Pages

When search engines store data, they compute the category of Web pages, and save

the category information of Web pages. This process is called the pre-categorization

of Web pages.

Firstly, determine the main category of a Web page, which is to extract Web page

topic according to the Web page contents, and compute the direct category vector of

Web page. Then, input the direct category vector, and compute the indirect category

vector according to category similarity table. At last, combine the direct category

vector with indirect category vector, and obtain the topic category vector of Web page.

The category similarity table is based on the inherent attribute of knowledge

categories. Its establishment can follow the domain ontology-based semantic

similarity computation model established in Chapter 5. The subcategories in the same

main category have higher similarity, and the similarity between different categories is

relatively lower.

6.2.2.2 Pre-categorization of Keywords

The process corresponding to the pre-categorization of Web pages is the pre-

categorization of keywords. When user input a keyword and retrieval Web pages

according to this keyword, according to the categories that the meaning of this

keyword belongs to, the categories that the search Web page belongs to will be

screened. Then, make screen to the Web pages according to chosen category. This

kind of two-level screen process composes the pre-processing model that implements

categorization technology.

The pre-categorization model of keywords is same as it of Web pages. Firstly, obtain

the main category of keyword according to keyword categorizer. Then, compute the

indirect topic category according to category similarity table. At last, obtain the

weight value of keyword in all category by adding direct and indirect topic categories.

53

Figure 10 Pre-categorization framework

Category similarity tableCategory aCategory bCategory c……Category aCategory bCategory c……

Web pages

Web categorizer

Compute direct topic vector

Compute topic category

Categorization resultCategory aCategory bCategory c……Web 1Web 2Web 3……

Category resultsCategory aCategory bCategory c……Keyword

Keywords

Keyword categorizer

Compute direct topic vector

Compute topic category

Direct topic category Direct topic category

Indirect topic category Indirect topic category

54

6.3 Modeling

6.3.1 Category Selective Mechanism

Because the amount of Web pages is huge and increasing explosively, the pre-

processing for searching process is particularly important. If the selection for

searching results can reduce the scope before searching all Web pages, it is helpful to

improve retrieval efficiency.

The process of selective mechanism is described as follow: Select the keyword

similarity coefficient , category similarity coefficient . When users

retrieval keyword, search engines firstly select the categories with the similarity

between to according to the category vector of retrieval keyword. Then, search

engines select the Web pages with the category similarity between to according

to the category vector of Web pages.

Though two-level categorization selective mechanism, after compare searching scope

with the category similarity of Web page according to keywords, the Web pages with

great difference in category are discarded. Because users are hardly concerned about

the information with low rank when browsing the result returned by search engines,

the pre-processed Web pages will not influence the searching performance in users’

view. And the amount of Web pages is greatly reduced, so the overhead of iterative

operation is reduced as well when computing rank

The selection of : When the value of tends to , the similarity of keyword

category is the highest, and only the direct topic category should be chosen to make

Web page selection. When the value of tends to 0, it is equal to not make category

selection.

55

The selection of : When the value of tends to , the similarity between Web page

category and keyword category is required to be the highest, and only the Web pages

with the same category are retrieval. Thus, the coverage rate of retrieval Web page is

relatively low, and it is easy to cause missing important information. When the value

of tends to , the similarity between Web page category and keyword category is

required to be the lowest, all the Web pages are retrieval, and degenerate to a selective

mechanism without pre-categorization.

6.3.2 Integrating HITS Algorithm with Categorization

The basic principle of Hits algorithm is that how many links that exterior refers to a

Web page represents the authority degree that this Web page has, which can be

measured by authority value ; how many links that a Web page refers to exterior

represents how much information that this Web page can provide as an information

center, which is the hub degree of this Web page, measured by hub value .

The relation between the authority value and hub value of Web page is shown in

Figure 10.

Figure 11 Relation between authority value and hub value

By several iterative computations according to the link structure of Web page, the

authority value and hub value of Web page can be obtained.

Consider the relating degree between the links of Web page and topic categories,

which the links with the target belonging to same or similar topic category are

1u2u1u

v1v

2v

3vu

56

considered to be more important.

Let the differential sum of Web pages and on each category vector as the

difference degree of the two Web page categories.

Compute the difference degree between the category attribute of all the Web pages ,

which referring to Authority Web page , with , expressed by

in the above formula, is the total category amount, is the difference

degree between Web pages , which referring to Authority Web page , and Web

page on category component . is the percentage of difference

degree. is the summation of difference degree ratio on each category

component, so as to obtain the difference degree on all categories.

Compute the difference degree between the category attribute of all the Web pages ,

which referred by Hub Web page , with , expressed by

in the above formula, is the total category amount, is the difference

57

degree between Web pages , which referred by Hub Web page , and Web page

on category component . is the percentage of difference degree.

is the summation of difference degree ratio on each category

component, so as to obtain the difference degree on all categories.

Where and is respectively the vector attribute of Web page and on

category , and is respectively the vector attribute of Web page and on

category . The values of , , , are generated in the pre-categorization

process of Web pages.

After normalizations:

According to the category difference degrees, compute the category similarities of

Web pages referred by links:

According to the formula above, modify the formulas of Authority value and Hub

value in HITS algorithm to:

58

6.4 Evaluation of Integrated Algorithm

The formula above integrates the category information of each Web page into HITS

algorithm, and the category relativity of link is considered while computing the link

structure of Web pages. Through this kind of method, the shortcoming that each link

between Web pages is treated equally in traditional HITS algorithm is overcome.

Combine the category information of Web pages themselves with link information,

and let it as the modified parameter of computation, thus, the accuracy of HITS

algorithm is improved.

In the aspect of computation complex degree, the application of categorization

information modifies the basic set of HITS algorithm by means of the pre-

categorization mechanism. Because HITS algorithm itself needs perform iterative

computation, the use of pre-categorization can greatly reduce computation overhead

by reducing Web page amounts, so as to improve the performance of HITS algorithm.

Meanwhile, the computation overhead of categorization process is , is the total

category amount. Because the magnitude of is always smaller than it of Web page

amount, the improved categorization-based HITS algorithm is better than HITS

algorithm on the complex degree of algorithms.

6.4 Summary

In this chapter, we perform improvement to HITS algorithm from two aspects, which

are the Web page pre-processing and analyzing the link structure of Web page, and

provide completive and detailed mathematics expression. After completive algorithm

expression and application, it shows that according to category information, more

accurate rank results can be obtained. The shortcoming caused by analysis and

59

computation only based on the link structure of Web page in HITS algorithm is

overcome, and the advantage that let Web pages in similar or same category have

higher relativity is obtained, so as to make rank algorithm simulate user habits when

browsing page in real time more accurate.

60

CHAPTER 7

Conclusion

In this paper, through the research and analysis on classical link structure-based

algorithms and their related improvements, we propose an improved HITS algorithm

based on categorization technology.

7.1 Summary of contributions

Improve domain ontology-based concept similarity computation based on five

decision factors.

We perform effective quantization to the five facts which have impacts on the

directed edge weight of ontology hierarchical network according to the characteristic

of ontology network, then make linear weighted combination according to the impacts

of these five categories of facts on directed edge weights, so as to quantify the

semantic similarity between the concept nodes in ontology network more

comprehensively.

Improve HITS algorithm based on categorization technology.

We integrate the category information of each Web page into HITS algorithm, and the

category relativity of links is considered when computing the link structure of Web

pages. Though this method, the shortcoming of traditional HITS algorithm is

overcome, which is each link between Web pages is treated equally. We combine the

category information of Web pages themselves with link information, and let it as the

61

corrected parameter of computation, so as to enhance the accuracy of HITS algorithm.

Besides, through the pre-categorization mechanism of category, the usage of

categorization information modifies the basic set of HITS algorithm. Because HITS

algorithm itself need perform iterative computation, the usage of pre-categorization

can greatly reduce computation overhead, and improve the performance of HITS

algorithm.

7.2 Future work

In this paper, we mainly discuss the theoretical researches and improvements of rank

algorithms for search engines, link structure-based algorithm is still the most common

rank technology being used now, which is relatively mature in the application of

commercial search engines.

With the passage of time, the personalized user-oriented search engine will certainly

take the place of the mainstream search engine using currently. However, the

personalized search engine need perform a long-period study in the aspects of

analyzing users’ habit in using search engines, the interesting topics, as well as some

other use characteristics, including the habits of choosing keywords, choosing results

from the returning set of query, the characteristics of query content and so on, so as to

become a personalized search engine accord to use habit which is a special

customization for users. In this paper, we only make limited exploration in the field of

user habit, which is also a general character, and the analysis and improved model

aiming at single user is not involved. Thus, the simulation of user behaviors is

relatively rough. In the field of researching personalized search engines, we need

obtain large amount of feedbacks and statistics data of users in a long period of time,

which can be accumulated and carried out in the further work.

62

References

Berners Lee, T, Hendler, J, Lassila, O 2001, ‘The semantic web’, Scientific American,

284(5)34-43.

Bharat, MK & Henzinger, R 1998, ‘Improved Algorithm for Topic Distillation in a

Hyperlinked Environment’, In Proceeding of {SIGIR}-98, 21st {ACM} International

Conference on Research and Development in Information Retrieval.

Brin, S & Page, L 1998, ‘The anatomy of a large-scale hypertexual web search

engine’, In Proceeding of the WWW7 Conference, page 107-117.

Broder, A, Kumar, R, & Maghoul, F 2000, ‘Graph structure in the web: experiments

and models.’ In Proceeding of the Ninth International World-Wide Web Conferecne.

Itsky AB & Hirst, G 2004, ‘Evaluating word net-based measures of lexical semantic

relatedness’, Computational Linguistics, 1(1);1-49.

Chakrabarti, S, Dom, B & Gibson, D 1999, ‘Mining the Link Stucture of the World

Wide Web’, IEEE Computer, 32(8).

Chakrabarti, S, Dom, B & Raghavan, P 1998, ‘Automatic resource compilation by

analyzing hyperlink structure and associated text’, In Proceeding of the Seventh

International World-Wide Web Conference.

Chakrabarti, S, van den Berg, M & Dom, B 1999, ‘Focused crawling: A new

approach to topic-specific web resource discovery’, In Proceedings of the Eighth

International World-Wide Web Conference.

63

Dean, J & Henzinger, RM 1999, ‘Finding related pages on the Web’, In Proceeding of

the WWW8 Conference, page 389-401.

Gan, KW & Wong, PW 2000, ‘Annotation information structures in Chinese texts

using how net’, Hong Kong: Second Chinese Language Processing Work shop, 85-

92.

Gruber, T 1993, ‘Ontolingua: A translation approach to portable ontology

specification’, Knowledge Acquisition 5(2), pp.199-200.

Haveliwala, HT 1999, ‘Efficient computing of PageRank’, Stanford Database Group

Technical Report.

Haveliwala HT 2002, ‘Topic-sensitive PageRank’, Proceedings of the Eleventh

International World Wide Web Conference.

Kleinberg, J 1999, ‘Authoritative sources in a hyperlinked environment’, Jouranl of

the ACM, 46(5):604-632.

Lawrence, S & Lee Guiles, C 1998, ‘Context and page Analysis for Improved Web

Search.’, IEEE Internet Computing, page38-46.

Ling, Z & Fanyuan, M 2004, ‘Accelerated evaluation algorithm: a new method to

improve Web structure mining quality’, Computer Research and Development,

41(1):98-103.

Mendelzon, OA & Rafiei, D 2000, ‘What do the neighbors think? Computing web

page reputations’, IEEE Data Engineering Bulletin, Page 9-16.

Ming, L, Jianyong, W & Baojue, C 2001, ‘Improved Relevance Ranking in

WebGather’, J. Cimput. Sci. & Technol. Vol.16 No.5.

Motwani, R & Raghavan, P 1995, ‘Randomized Alogrithms’, Cambridge University

Press.

Neches, R, Fikes, R, Finin, T, Gruber, T, Patil, R, Senator, T & Swartout, WR 1991,

‘Enabling technology for knowledge sharing’, AI Magazine 12(3), pp.36-56

64

Page, L, Brin, S & Motwani, R 1998, ‘The PageRank citation ranking: Bringing order

to the Web’ Technical report, Computer Science Department, Stanford University.

Qianhong, P & Ju, W 1999, ‘Attribute theory-based text similarity computation’,

Computer Journal, 22(6):651-655.

Qun, L & Sujian, L 2002, ‘CNKI-based word semantic similarity computation’,

Computer Linguistics and Chinese Information Processing, 2002(7):59-76

Steichen, O & Daniel-Le, C 2005, ‘Bozec. Computation of semantic similarity within

an ontology of breast pathology to assist inter-observer consensus’, Computers in

Biology and Medicine, (4):l-21.

Studer, R, Benjanmins, VR & Fensel, D 1998, ‘Knowledge engineering: principles

and methods’, Data and knowledge engineering 25, pp.161-197.

Sujian, L 2002, ‘Research on semantic computation-based sentence similarity’,

Computer Engineering and Application, 38(7):75-76.

Weizhu, C, Ying, C & Yan, W 2005, ‘Categorization technology-based rank

algorithm for search engine – Category Rank’, Computer Application, 2005(5).

Xiaofeng, Z, Xinting, T & Yongsheng, Z 2006, ‘Ontology technology-based Internet

intelligent search research’, Computer Engineering and Design, 27(7):1194-1197.

Xuelong, W, Xuemei Z & Xiangwei L 2006, ‘Application and improvement of time

parameter in Hits algorithm’, Modern Computer.

Zhihong, D & Shiwei, T 2002, ‘Ontology research review’, Beijing University

Journal (Natual Science Edition), (5):730-738

65

APPENDIX A

Semantic Relations

In table 2, represents sentence, represents noun phrase, represents

individual noun phrase, represents category noun phrase, represents verb

phrase, represents original verb phrase, and represents prepositional

phrase. All the verb forms in rules include various kinds of deformations of their

verbs.

Seq.

No.

Semantic

relation

Extraction rule

1 ISA If

Then

2 AKO If

Then

3 Have If

Then

4 Can If

Then

5 Is If

Then

6 Part-Of If

Then

7 Composed

-Of

If

66

Then

8 Belong-To If

Then

9 Time If

Then or

10 Position If

Then

11 OthersIf

Then

Explanations:

Rule 1: If sentence can be expressed in form of , and is an

individual noun phrase, NP2 is a category noun phrase, then the semantic relation can

be extracted as .

Rule 2: If sentence can be expressed in form of , and is a

category noun phrase, as well as , then the semantic relation can be extracted as

.

Rule 3: If sentence can be expressed in form of , then the

semantic relation can be extracted as .

Rule 4: If sentence can be expressed in form of , then the semantic

relation can be extracted as .

Rule 5: If sentence can be expressed in form of , then the semantic

relation can be extracted as .

Rule 6: If sentence can be expressed in form of , then

the semantic relation can be extracted as .

Rule 7: If sentence can be expressed in form of or

, then the semantic relation can be extracted as

.

Rule 8: If sentence can be expressed in form of or

, then the semantic relation can be extracted as

67

.

Rule 9: If sentence can be expressed in form of , then the semantic

relation can be extracted as or .

Rule 10: If sentence can be expressed in form of , then the

semantic relation can be extracted as .

Rule 11: If sentence can be expressed in form of , and not

, then the semantic relation

can be extracted as .

68

APPENDIX B

“Linear Structure” Ontology

Linear Structure

Name Cardinality Type Other Facets

Attribute Multiple String Value=Stack, Linear List, String, Queue

Depth Required Single Integer Minimum=1, Maximum=4, Value=1, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=1.0, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=7.03, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.4, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Stack

Name Cardinality Type Other Facets

Attribute Multiple String Value=Linear Structure, Linked Stack, Sequential Stack

Depth Required Single Integer Minimum=1, Maximum=4, Value=2, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.18, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=8.61, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.2, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Linked Stack

Name Cardinality Type Other Facets

Attribute Multiple String Value=Stack

69

Depth Required Single Integer Minimum=1, Maximum=4, Value=3, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.12, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=9.02, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Sequential Stack

Name Cardinality Type Other Facets

Attribute Multiple String Value=Stack

Depth Required Single Integer Minimum=1, Maximum=4, Value=3, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.12, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=9.03, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Linear List

Name Cardinality Type Other Facets

Attribute Multiple String Value=Linear Structure, Sequential storage structure, Linked

storage structure

Depth Required Single Integer Minimum=1, Maximum=4, Value=2, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.53, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=8.92, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.2, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Sequential storage structure

Name Cardinality Type Other Facets

Attribute Multiple String Value=Linear List, Static Allocation, Dynamic Allocation

Depth Required Single Integer Minimum=1, Maximum=4, Value=3, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.24, default=0.0

70

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=9.31, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.2, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Static Allocation

Name Cardinality Type Other Facets

Attribute Multiple String Value=Sequential storage structure

Depth Required Single Integer Minimum=1, Maximum=4, Value=4, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.18, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=10.46, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Dynamic Allocation

Name Cardinality Type Other Facets

Attribute Multiple String Value=Sequential storage structure

Depth Required Single Integer Minimum=1, Maximum=4, Value=4, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.18, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=11.46, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Linked storage structure

Name Cardinality Type Other Facets

Attribute Multiple String Value=Linear list, Single linked list, Circular linked list,

Double linked list, Double linked circular list

Depth Required Single Integer Minimum=1, Maximum=4, Value=3, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.35, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=9.81, Default=0.0

71

Out degree Required Single Float Minimum=0.0, Value=0.4, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Single Linked List

Name Cardinality Type Other Facets

Attribute Multiple String Value=Linked storage structure

Depth Required Single Integer Minimum=1, Maximum=4, Value=4, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.18, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=13.46, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Circular Linked List

Name Cardinality Type Other Facets

Attribute Multiple String Value=Linked storage structure

Depth Required Single Integer Minimum=1, Maximum=4, Value=4, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.18, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=10.36, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Double Linked List

Name Cardinality Type Other Facets

Attribute Multiple String Value=Linked storage structure

Depth Required Single Integer Minimum=1, Maximum=4, Value=4, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.18, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=12.46, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Double Linked Circular List

72

Name Cardinality Type Other Facets

Attribute Multiple String Value=Linked storage structure

Depth Required Single Integer Minimum=1, Maximum=4, Value=4, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.18, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=10.86, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

String

Name Cardinality Type Other Facets

Attribute Multiple String Value=Linear structure

Depth Required Single Integer Minimum=1, Maximum=4, Value=2, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.06, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=8.41, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Queue

Name Cardinality Type Other Facets

Attribute Multiple String Value=Linked storage structure, Circular Queue, Linked

Queue

Depth Required Single Integer Minimum=1, Maximum=4, Value=2, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.18, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=8.5, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.2, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Circular Queue

Name Cardinality Type Other Facets

Attribute Multiple String Value= Queue

73

Depth Required Single Integer Minimum=1, Maximum=4, Value=3, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.12, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=9.5, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

Linked Queue

Name Cardinality Type Other Facets

Attribute Multiple String Value= Queue

Depth Required Single Integer Minimum=1, Maximum=4, Value=3, Default=1

Importance Degree Required Single Float Minimum=0.0, Maximum=1.0, Value=0.12, default=0.0

In degree Required Single Float Minimum=0.0, Value=0.1, Default=0.0

Information Amount Required Single Float Minimum=0.0, Value=9.61, Default=0.0

Out degree Required Single Float Minimum=0.0, Value=0.0, Default=0.0

Semantic Relation Required Single String Value=Entirety-part

74