IiWAS2005 - Ontology Support in Supply Chain Environment

8/14/2019 IiWAS2005 - Ontology Support in Supply Chain Environment

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ONTOLOGY SUPPORT IN A SUPPLY-CHAIN

ENVIRONMENT

Corey Chong1, Angela Goh1 and Puay Siew Tan2

Abstract

In a supply chain environment, interoperability has been a longstanding issue for application

developers and e-Business supply chain collaborators. A major problem lies with semantic

differences in business terms used by e-business transactions. This paper focuses on how

interoperability is enhanced through the use of ontologies. Leveraging on existing efforts to

standardize supply chain transactions (specifically, RosettaNet), an experimental ontology is built

using W3Cs semantic language OWL. In order to demonstrate the use of the ontology, a schemamatching system was designed by deriving relationships between business terms using WordNet

and their context structure in the schema.

Keywords: web services, ontology, RosettaNet, interoperability, schema matching

1. Introduction

In the supply chain domain, one of the main challenges for developers is interoperability between

applications used in facilitating daily business processes. Traditionally, supply chain business is

carried out using snail mail, telephones and facsimile systems to exchange information and processtransactions. Early attempts to automate supply chain collaboration include Electronic Data

Interchange (EDI) [18] and information hubs. It is reported in [8] that inflexibility of EDI in

representing business processes made it limited to the largest 20% of trading partners. In order to

achieve full automation in the supply chain domain, collaborating partners must agree to a standard

protocol to exchange information and execute business transactions. Standardization efforts such as

RosettaNet [15] and ebXML [4] emerged as a result. The RosettaNet consortium was formed to

tackle the longstanding issue of interoperability between supply chain partners. RosettaNets

mission is to drive collaborative development and rapid deployment of e-business standards and

services, creating a common language and open processes that provide measurable business

benefits for global trading networks. One difficulty in implementing RosettaNet standards is the

complexity involved in understanding, developing and testing the RosettaNet Interface Framework(RNIF) and Partner Interface Processes (PIPs) [15]. To date, there are only about 500 large

corporations (e.g. Fujitsu, Microsoft, IBM, etc.) that are RosettaNet compliant partners. In an

automated environment, RosettaNet uses servers to exchange information over the Internet. XML

[22] functions as the alphabet, and electronic commerce applications serve as the vehicle through

which e-business processes are transmitted. The lack of agreement on the words, grammar and

dialog that constitute e-business processes illustrates the need for standards. RosettaNet

dictionaries provide the words, the RosettaNet Implementation Framework (RNIF) acts as the

grammar (predefined protocol) and RosettaNet Partner Interface Processes (PIPs) form the dialog.

The collaborative decision support solutions (DSS) used by trading parties will need to be aligned

1School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 6397982 Web Services Programme, Singapore Institute of Manufacturing Technology (SIMTech), 71 Nanyang Drive, Singapore 638075


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with their business protocol. RosettaNet Partner companies realize that consortium PIPs only

specify the process at the point of interface, and the true value lies in aligning internal decision

systems with the PIP specifications.

In this paper, we attempt to embed business concepts and terms in an ontology [5]. OWL [10] is anontology Web Language recommended by W3C [19]. With the use of OWL functions such as

Class, SubClassOf, EquivalenceOf, Datatypes Properties, Object Properties, and a host of other

features, business terms and their relationships can be captured. These OWL-stored concepts can be

manipulated, matched, reasoned and queried upon, according to different business needs and

application requirements. Thus, the ontology will reduce the limitations RosettaNets PIPs face in

representing business concepts in XML (both its domain structure and document structure of the

data). The RosettaNet consortium is aware of these limitations and have plans to create machine-

readable schemas in current work plans. Representing concepts in a semantic language allows

knowledge-sharing, extension of concepts across several ontologies and provides mapping

capability for overlapping concepts in distinct ontologies. Moreover, semantically difficult queries

can be answered, via inference or aggregation through the ontology.

Hence, the motivation of this paper is to address interoperability problems between supply chain

partners in terms of the different terminology used in e-Business transactions. It is common that

enterprises have their own set of business terms, terminology and meanings based on local context.

The main issue being addressed is the construction of ontolgies from PIP. To demonstrate the use of

the ontology, a schema matching application has been designed and tested. The system is Web

Services based and is capable of matching inbound business schemas with ontologies stored in local

repository.

The following section introduces RosettaNet, the basis upon which the ontology is created. Section3 describes the approach taken to build the ontology. A scenario is presented in section 4, which

illustrates the use of the ontology. Section 5 briefly describes the schema matching algorithm used,

followed by test results and conclusions in sections 6 and 7 respectively.

2. RosettaNet and Motivation for Building an Ontology

RosettaNets PIP Specification Package presents concepts and knowledge in three interdependent

forms: RosettaNet PIP Message Guidelines, RosettaNet XML Message Schema in Document Type

Declaration (DTD) format [21] and RosettaNet Implementation Framework (RNIF) guideline. The

Specifications provide the business performance controls (also known as the choreography of the

exchange) as well as define the purpose of the business process and the roles that participate in theprocess. The Message Guidelines define the cardinality, vocabulary, structure, and allowable data

element values and value types for each message exchanged during the execution of a PIP. The

DTD provides the order or sequence of the elements, element naming, composition, and attributes.

In order to implement a RosettaNet business exchange, the above must be adhered to. However,

limitations are present in these monolithic PIPs. The Message Guidelines define the RosettaNet

Message structure using a hierarchical or tree presentation and the DTD is based on information

from the Message Guidelines. Due to limitations of DTD, point-to-point consistency cannot be

captured by the DTD alone. For example, if an element is utilized two times within the Message

Guideline with different sub-element cardinalities, the DTD cannot express this constraint.

Therefore, DTD will present the less restrictive cardinality to support both occurrences. Thebusiness knowledge embedded in the PIPs Message Guidelines and XML Message Schemas will


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be leveraged upon. This data will be stored within a single OWL ontology depending on the

functionality of the PIP. For example, Request Quote PIP has two data files in its Specification

Package: RosettaNet XML Message Guidelines HTML file and a Message Schema DTD file

depicting the usage/definition and document structure of the business terms respectively.

An ontology stores RosettaNets information as a compact knowledge base in the form of a single

file complete with all descriptive text, cardinalities and elements hierarchical information. For

example, Entity Instances provide a list of possible values and their descriptions for a specific

business term. An information system administrator will usually insert these values into the

backend database system. The values can be displayed to users when completing an online form.

For example, if the term in question is Global Country Code, a list of codes recognized globally

will be displayed along with their descriptions. Entity information is extracted from RosettaNets

PurchaseOrder-Notification Message Guideline and inserted into OWL ontology. OWL allows a

Class element to contain direct instances, which in turn can store comments regarding instance

description as well.

Other information stored in the Entity Instances section require domain knowledge in order to

facilitate the completion of RosettaNet-compliant transaction. In order to automate the process, it is

necessary for the server to provide all possible instances. This prevents errors and misunderstanding

from occurring, leading to greater supply chain collaboration. It is therefore possible that if a

transaction has compulsory fields left uncompleted, the system can provide a feature by extracting

the RosettaNet instances from the ontology and display them as choices in the web application

interface for the client to choose.

3. Building the Ontology

The RosettaNet PIPs Extraction module is used to facilitate extraction of data from PIPs and to

insert them into a preliminary OWL file. The extraction module gathers information from

RosettaNet files, sorts the data and stores them in memory. Thereafter, these in-memory data are

processed and inserted in the correct order into an OWLWriter. The flowchart in Figure 1 depicts

the program flow of this extraction module. This preliminary OWL file requires trimming and some

minor modifications to convert it into a well-formed expression. This is because concepts that are

meant to be expressed in OWLs Object or Datatype Properties require human interpretation and

cannot be done satisfactorily by the system alone. A richer expression and definition of a concept

term can be provided through manual modification. Examples of modification which cannot be

achieved by automated means include the changing of Classes to Properties instead of Class

Definition, concepts such as FreeFormText into XMLSchema String data-type and so on. Protg -

OWL Editor [13] with graphical user interface is used to aid in this manual task.

The methodology in creating RosettaNets OWL ontology from its PIPs requires the use of XML

Parsers / Converters and OWL Parsers / Reasoners. First, the pre-processing procedure converts

RosettaNet DTD files into XML Schemas. This will facilitate the generation of a tree structure

which in turn will be accessed to construct the ontology. Although there are open-source tools

(OWL-API [11] and Jena 2.1 [7]), their functionalities are limited and lack the ability to create a

new ontology and insert data into it.


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Figure 1. Phases in Ontology Construction

The module therefore produces a raw ontology file that is built by writing the large amount of PIP

data into the OWL file without considering tricky semantic language issues. These issues will be

tackled manually. We transfer the in-memory data obtained from preprocessing efforts into a

sequential writer that prints data according to OWLs definition syntax. Upon obtaining the

untrimmed version of the RosettaNet ontology, we proceed with manual modification to obtain a

well-formed ontology. This manual process is achieved using Protg-2000 OWL ontology Editor

as shown in Figure 2. It can be observed that Asserted Hierarchy on the left of the GUI displays

Class Hierarchy structure of RosettaNet business concepts. A Class element is denoted by this

symbol . All Classes are subclass of OWL Class Thing and the hierarchy structure depicts theclass-subclass relationship between concepts. The PropertiesFramein the upper right of the GUI

contains information regarding the Class elements Object Properties ; Datatype Properties ;

and rdfs:comments (lower right of the GUI) contain definitions of the Class elements obtained

from the extraction of RosettaNet files. It is with the use of this GUI that the ontology is modified

to include Datatype Properties and Object Properties linking to their domain terms. Clearly, this

process is difficult to achieve fully automatically. We are, however, able to eliminate the laborious

task involved in constructing an ontology by inserting all the RosettaNets Class element names,

comments, data types, and instances using the OWLWriter. Conversion of business terms to

Datatype and Object Properties is then done manually.

4. Scenario

There are more than 200 RosettaNets PIPs Specifications in existence, with other new ones still

being created. Therefore, as a test case, we have selected Purchase Order and Delivery Order

(equivalent to Shipping Order for RosettaNet context) transactions in a simulated environment.

This smaller scope allows us to analyze and identify the accuracy of the matching system using

RosettaNet PIP as our designated source schema.

Creating Ontology from RosettaNet PIP Package

PIP Package

Extractors

In Memory

OWL Writer

XML DTD

File

Message

Guidelines

XML

Parsing

HTML

Parsing

Business

Properties

Add Class Add

InstanceAdd

SubClassOf

Fundamental

Representation

Fundamental

Business Data

Entities

Business

Data

Entities

Add

Cardinality

Add

Comments


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Figure 2. Editing the Ontology

A scenario that is described in Figure 3 is used to illustrate the system that has been developed. It

comprises of an electronic communication between a Multi-National Corporation (MNC) who is a

RosettaNet compliant partner and a Small-Medium Enterprise (SME). The scenario starts with (1)the MNC issuing a Purchase Order (PO) request. The RosettaNet Server intercepts this request on

behalf of the SME and matches the PO request with stored RosettaNet ontologies. It then (2) sends

a query to the SME that includes information that the SME administrator must provide (3) so that

theRosettaNet Serveris able to extract out a subset correctly. Thereafter, a modified PO (4) is sent

to the SME. A Delivery Order (DO) is assumed to be issued by the SME and directed to an

appropriate Delivery Party through theRosettaNet Serveralso, though not shown in Figure 3. PIP

3A13: Notify of Purchase Order Information [16] and PIP 3B11: Notify of Shipping Order [17]

specifications from RosettaNet are used, and converted into W3Cs OWL ontologies to employ

machine translation for the business exchanges.

Figure 3. Scenario in the supply chain environment

Multi-National

Corporation

(MNC)

RosettaNet Server

Mapping

Decision

1. PO

(Original)

4. PO

(Modified)

3. Response

2. Query

Small Medium

Enterprise

(SME)

Multi-National

Corporation

(MNC)

RosettaNet Server

Mapping

Decision

1. PO

(Original)

4. PO

(Modified)

3. Response

2. Query

Small Medium

Enterprise

(SME)


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Figure 4 illustrates the flow of messages in a simulated scenario involving the Purchase Order

business process. A customer (whom we assume is a MNC) would issue a RosettaNet compliant

Purchase Order to a designated supplier (a SME). Once the Supplier is able to fulfill the order, a

Delivery Order is issued to a designated Delivery Party for transfer of goods to the customer. The

gateway for translating business schemas is aRosettaNet Serverthat utilizes the Java API for XMLMessaging (JAXM) with a schema matcher module. In this scenario, we assume that MNCs use

RosettaNet compliant systems, while SMEs have their own set of business schemas and business

taxonomies. The RosettaNet Server acts as an intermediary, translating and mapping business

terms suitable for each individual party.

Figure 4. Scenario Implementation Diagram

5. Schema Matching

The scenario described in section 4 is a vehicle to illustrate the use of ontologies in a supply-chain

environment. The ontology, which is based on the RosettaNet PIPs are compared with in-coming

non-RosettaNet schemas. This is done by computing their semantic distances in terms of their

schema structure and taxonomy similarities. These generated distances are used to produce a set of

classification rules based on decision tree induction. In the schema matcher process, there are twoapproaches used, namely, direct and indirect methods which are adapted from Jackman [6] and Xu

and Embley [23]. Figure 5 shows the components of the system and their relationship.

5.1 Direct Method

The direct matcher uses the structure of the word or sentence, looking for similarity in strings. It is

used in two ways. Firstly, direct matching is adopted when the RosettaNet Server accesses and

retrieves the SOAP header upon receipt of a SOAP message from the client. The header will

contain information regarding the content of its message. For example, if the header contains

PurchaseOrder, the system will search in a predefined ontology directory for the stored OWL files

and retrieve PurchaseOrderInformation-Notification.owl which has the closest match compared toother files in that directory. Secondly, direct matching is used when simple straightforward


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matching is possible. For example, comparing between term pairs names and word nouns stored in

the Matching Table which is described in further detail below.

Figure 5. Components of the Schema Matching System

Table 1 shows that heuristic values defined for the Direct Threshold is set at 0.5 (determined

empirically). In File Matching, if the file retrieved has OWL file extension (*.owl) and the final

confidence score exceeds the threshold, the target ontology file is retrieved and loaded. For

example, PurchaseOrderNotification matched against PurchaseOrder will generate a value of

0.6. This value has taken into account the hit ratio of alphanumeric characters, which is 0.4 in this

case (purchase and order each contribute 0.2), and the confidence increases by 0.2 when they arestructured in correct sequential order in both sources. Since the score obtained is greater than

Direct Threshold 1, this file is chosen.

Table 1. Thresholds for Direct Matching

Direct

Threshold 1

Direct

Threshold 2

Increment

Confidence

Value

Sequential

Value

Remarks

File

Matching0.50 NA +0.20 +0.20

Used in comparing ontology files

with SOAP header to pinpoint

current business transaction.

Term

MatchingNA 0.50 +0.20 +0.20

Used in simple straightforwardmatching of word terms. (i.e. match

table names with ontologys Class

names)

Direct matching is generally used in WordNet [9] for operations involving retrieval of word nouns

from the database. A database table has individual noun-pairs, with each pair given a WordNet

score. Therefore, in order to compute the average WordNet score for business terms like

OrderQuantity, the system locates corresponding lists of words (i.e. order and quantity) in the

table matching that contains that noun. Another usage for direct matching is as follows: For

example, action-quantity term pair has a positive WordNet score. Suppose quantity is from aRosettaNet data field, all the OntClasses that have names containing quantity would be retrieved.


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One such OntClass will be OrderQuantity in RosettaNet and this will be involved in Context

matching. Clearly, these two usages require only a straightforward character matching, thus Direct

Matching is used. More details on WordNet and Context methods are described in the next section.

5.2 Indirect Method

Under indirect matching, two confidence scores for source to target element matching are

computed, namely, WordNet score and Context score. The first method provides estimated

mappings while the second method confirms the final source to target mapping. RosettaNet is the

source schema and target schema is a sample representing organizations that are non-RosettaNet

compliant. The former will be referred to as source object sets, SOS and while the latter is termed

target object sets, TOS.

The WordNet Method computes confidence scores for terms used in SOS and TOS based on their

hypernym hierarchy. The hypernym hierarchy contains concepts that define the more general

classes of entities of the original term. Word sense defines the various meanings that a term can

have in the English language. For example, the word company has meanings like an institution

which is an organization founded and united for a specific purpose, or in a more general sense, an

organization where a group of people who work together. Each distinct word sense has its own

hypernym hierarchy in WordNet. The system has adopted metrics from Jackman [6]. including

NumberOfRootTerms, XYSenseCount, MinSenseCount, MaxSenseCount, etc. More details on

WordNet can be found in [2].

In Structural Context method, a TOS and a SOS match only if the values of their adjacent object

sets around the object schema element are similar. With reference to Figure 6, TOS refers to

Supplier business term and Adjacent TOS refers to the subclasses under it that further defines thebusiness concept. This similarity is measured by computing metrics on their relationship. Further

details can be found in [6].

Figure 6. Object Set Diagram


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6. Results and Discussion

To investigate the effectiveness of the system developed, testing was conducted and the results

were assessed using the quality measures, Recall and Precision [3]. The WordNet method requires

the computation of a confidence score from the metrics mentioned in section 5. In order to builddecision trees, we have to provide training data from RosettaNet. Testing was carried out using the

following four XML Schemas:

papiNet [12]: papiNet, the Global Transaction Standard for the Paper and Forest Supply Chain

standard provides a small XML Schema to support business transactions in this specific vertical

industry.

XML Industry Project [20]: The XML Working Group under the of National IT Standards

Committee proposed XIP to aid companies, especially Small and Medium Enterprises (SMEs),

to revamp their business processes with XML technology. The aim of the project is to encourage

SMEs to employ XML technology and benefit from doing so.

Quote Messaging Standard (QMS) [14]:The QMS Quote is a large schema used by the automotive

industry for quotation and invoicing purposes.

BizTalk [1]: The business schema taken from Microsofts BizTalk server site is another example of

attempts at standardizing information exchange

Results are obtained with the assumption that a human expert has classified the training file. Given

the number of direct and indirect matches N determined by a human expert, the number of correct

direct and indirect matches C and the number of incorrect matches I, the metrics are computed as

follows: recall ratio R = C/N and the precision ratio P = C/(C + I),

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

papiNet XIP QMS biztalk

Precision

Recall

Figure 7. Performance results

As seen in Figure 7, the results range from moderate (70% recall and 50% precision) to poor. This

is mainly due to a large number of incorrect mappings. It should be noted that mapping decisions

depends largely on the type of schemas used.

As seen from the testing results, match performance is better when the two schemas being

compared are of similar size. This may be due to the fact that similar size schemas tend to have

similar tree structures and nesting of leaf nodes. Thus, the results are better when matching the two

large schemas of RosettaNet and QMS Quote. However, performance deteriorates when matching

schemas of different sizes. This may be due to the huge number of synonyms being generated for


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the source elements. Furthermore, the structure of small and large schemas differ, resulting in low

scores for structural matching.

Another problem is the system does not take into consideration mappings for leaf node elements.

Class elements such as Country, Street and City, which are leaf node sub-Classes of Address Classnaturally maps to the source schemas PhysicalLocation sub-Classes, but such mapping decisions

are not generated in the results. It is also unable to differentiate between mappings of shipTo,

BillTo and SoldBy Classes, which all have PartnerDescription as their only sub-Class. This is

related to the earlier point regarding schemas of different structures.

The limitation within context matching arises when there is a lack of child nodes (adjacent object

sets) to do comparison. Clearly, this will generate a zero Context score for the object sets

concerned.

In the supply chain context, there is a wide usage of acronyms or abbreviations in business

schemas. For example, UOM, which stands for unit of measure, can be found widely in BizTalks

PO schema. The use of acronyms makes machine matching difficult and nullifies the results of

WordNet and Context matching methods. To solve this problem, a data dictionary which contains

domain acronyms, could be used during machine translation.

7. Conclusion

An OWL ontology based on RosettaNet Notify of Purchase Order Information (3A13) PIP has

been built successfully. The method and tools used can be readily applied to any other PIPs to

create ontologies in other business areas. When a new XML schema is received by an organisation,

the availability of an ontology allows easy extraction of Class element information and parsing ofthe ontology. A demonstration of the use of the created ontology was given. This involved schema

matching between two organisations in a supply chain scenario. Future work includes the creation

of ontologies based on other XML standards in various domains.

References

[1] BizTalkhttp://www.microsoft.com/biztalk/[2] DIDION, J. and BARTON, G. Java WordNet Library API. http://sourceforge.net/projects/jwordnet[3]

DO, H.H. and RAHM, E. COMA A System for Flexible Combination of Schema Matching Approach. 28

th

International Conference on Very Large Data Bases, Hong Kong, 2002.

[4] ebXML Specifications. OASIS Consortium. http://www.ebxml.org/specs/index.htm.[5] HORROCKS, I., PATEL-SCHNEIDER, P. F. and HARMELEN, F. V. From SHIQ and RDF to OWL: the

making of a Web Ontology Language. 13th International WWW Conference, New York, USA, 2004

[6] JACKMAN, D. Mapping Target Schemas to Source Schemas Using WordNet Hierarchies and StructureContext. Department of Computer Science, Brigham Young University. www.deg.byu.edu/papers/ 2002

[7] JENA Version 2.1: Java Framework for Building Semantic Web Applications. HP Labs Semantic WebResearch. http://jena.sourceforge.net/javadoc/index.html

[8] KAK, R. and SOTERO, D. Implementing RosettaNet E-Business Standards for Greater Supply ChainCollaboration and Efficiency. http://www.edifice.org/ERUG/i2-eBusiness-Collaboration-whitepaper.pdf, 2002.


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[9] MILLER, G. WordNet: a lexical database for English. Communications of the ACM 38 (11), 1995[10] OWL Web Ontology Language http://www.w3.org/2004/OWL/[11] OWL-API: High-level view of an OWL ontology based on the OWL Abstract Syntax

http://wonderweb.man.ac.uk/owl/

[12] papiNet. Global Transaction Standard for the Paper and Forest Supply Chain standard. http://www.papinet.org/[13] Protg OWL GUI Editor. http://protege.stanford.edu[14] QMS Quote Messaging Standard http://www.xml.org/xml/registry_searchresults.jsp?industry=7&keyword

=&update_date=7200&schema_type=0#622

[15] RosettaNet http://www.rosettanet.org/[16] RosettaNet PIP 3A13: Notify of Purchase Order Information, http://www.rosettanet.org/rosettanet/Rooms/

DisplayPages/LayoutInitial?Container=com.webridge.entity.Entity%5BOID%5BAB36B6A84BD54B4E850B93

E351D2DEF2%5D%5D

[17] RosettaNet PIP 3B11: Notify of Shipping Order Notification, http://www.rosettanet.org/RosettaNet/Rooms/DisplayPages/LayoutInitial?Container=com.webridge.entity.Entity%5BOID%5B01F84A8AA6C04B4D9DC5E

919A05355BF%5D%5D

[18] SOKOL,P.K. From EDI to Electronic Commerce: A Business Initiative, 2nd Ed, Mcgraw-Hill, 1995.[19] W3C World Wide Web Consortium http://www.w3.org/[20] XIP ITSC-XML Working Groups XML Industrial Project.: http://www.itsc.org.sg/downloads/xip/

xip_index.html

[21]

XML DTD Specification http://www.w3.org/TR/2001/REC-xmlschema-1-20010502/xmlspec-19990429.dtd

[22] XML Extensible Markup Language http://www.w3.org/XML/[23] XU, L. and EMBLEY, D. W. Discovering Direct and Indirect Matches for Schema Elements. 8th International

Conference on Database Systems for Advanced Applications (DASFAA'03), Kyoto, Japan, 2003.

IiWAS2005 - Ontology Support in Supply Chain Environment

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