Extracting Semantic Constraint from Description Text for

Semantic Web Service Discovery

Dengping Wei, Ting Wang, Ji Wang, and Yaodong Chen

Reporter: Ting WangDepartment of Computer Science and Technology

School of ComputerNational University of Defense Technology, China

tingwang@nudt.edu.cn

Outline

Motivation Semantic Constraint for SWS Discovery Extracting Semantic Constraint Matching Algorithm Experiment Results Conclusions and Future Work

Motivation Various Semantic Web Service (SWS) des

cription ontologies or frameworks e.g. OWL-S, WSMO, WSDL-S, SAWSDL.

Various SWS matchmakers logic based semantic IOPE matching

inputs(I), outputs(O), preconditions/assumptions(P) and effects/postconditions(E)

logic based semantic Input/Output matching …

Motivation

Most current SWS matchmakers treat the SWS signature as a set of concepts not sufficient to discover SWS two services with similar semantics may fail to

match two services with the same input and output

concepts may have essential differences in semantics

which may not be detected by logic based reasoning.

Motivation Many recent researches have explored various

information to complement service I/O concepts for SWS matchmaking The ranked matching algorithm [Jaeger, et al. 2005] A hybrid method for SWS discovery [Klusch, et al.

2006] SWS matchmaking based on iSPARQL [Kiefer, et al.

2008] [Hull, et al. 2006] describes the relationships and uses

OWL ontologies

Motivation

The relationships between the service I/O concepts can be helpful for expressing the semantics of services.

Motivation

Our idea: add some restriction relationships to the interface con

cepts to enhance the semantic description of services.

extract restriction relationships those relationships not defined in the domain ontology. from the service description text automatically

perform the matching on the service I/O concepts and their semantic constraints represented by a constraint graph

Outline

Motivation Semantic Constraint for SWS Discovery Extracting Semantic Constraint Matching Algorithm Experiment Results Conclusions and Future Work

Semantic Constraint for SWS Discovery Observation:

the domain of concept is not specified the price of a book the price of a flight ticket

the property of concept is not specified the food with the maximum price the food with brand “Coca Cola”

the relationship between concepts is not specified

the food contained in a certain grocery store the food sold by a certain grocery store

Semantic Constraint for SWS Discovery

The semantics of SWS will be better clarified if the constraint relationships of the

concepts have been annotated

Semantic Constraint for SWS Discovery

Definition of a statement <SC,CT,OC> SC (Subject Concept)

subject of the statement usually corresponds to the service I/O concepts.

OC (Object Concept) object of statement described as another concept or a literal.

CT (Constraint Type) predicate of the statement identifies the property or characteristic of the subject concept

that the statement specifies.

Constraint Types Definition CT (Constraint Type)

three important abstract constraint types isPropertyObjectOf Constraint:

triple <A, isPropertyObjectOf,B> means that concept A is a property object of concept B.

hasPropertyObject Constraint: this constraint relation is the inverse of isPropertyObjectOf.

Operation Constraint: triple <A, Operation, B> means that two concepts entities h

ave a certain association between them < Book, “published by”, “Springer” > the books that are published by Springer

Constraint Graph Definition

DefinitionLet C be a set of concepts, a directed constrai

nt graph can be described as ConstraintGraph(C) = {<SC,CT,OC>|SC C}∈ .

Outline

Motivation Semantic Constraint for SWS Discovery Extracting Semantic Constraint Matching Algorithm Experiment Results Conclusions and Future Work

Extracting Semantic Constraint

NP VP

DT NN VBZ NP(price)

NP(price) PP

IN NP(book)DT NN

service returns

the price

book

S

The

NP(book) VP

VBD PP

publishedIN NN

DTthe

NN

Springer.by

Preprocessing

Parsing

Extracting

Constraint Graph

Syntactic Tree

The service returns the price of the book published by Springer.Description text

<SC1,CT1,OC1>

<SC2,CT2,OC2>

……..

<SCn,CTn,OCn>

<Price, isPropertyObjectOf , Book><Book, “published by”, “Springer”>

(a) semantic constraint extracting framework (b) example

of

Fig. 2. Semantic constraint extraction

Stanford PCFG Parser

Extracting Semantic Constraint

Candidate Constituent Detection Constraint Constituents Filtering Extracting Modifier

Extracting Semantic Constraint Candidate Constituent Detection

observation: the constraints of a key-word are probably contained in the

phrase whose head word is the keyword.

detect all such phrases by propagating the key-word from the bottom to the top of the syntactic tree.

the propagation path is expressed as a sequence of interior nodes in the parsing tree

e.g. a node sequence “NP NP” in the example is the propagation path of key-word “price”.

Constraint Constituents Filtering and Extracting Modifier

Outline

Motivation Semantic Constraint for SWS Discovery Extracting Semantic Constraint Matching Algorithm Experiment Results Conclusions and Future Work

Matching Algorithm

Constraint Graph Matching(CGM)

where P is the number of triples in ConstraintGraph(Cr )

P’ the number of triples in ConstraintGraph(Cs) function TripleMatch(RTi, STi) to estimate the matc

h between two triples RTi and STj.

))/P), STtch(RT((TripleMa), C(CGraphMatchConstrain ji

P

iP'j

sr

1

maxt

Matching Algorithm

Triples Matching two triples are matched and the degree of

match can be measured if all the three elements in each triple are relative

Matching Algorithm Concept Matching

matching: five different levels Exact match: r = s. Plug-in match: r Ascendant∈ (s) s Descendant∨ ∈ (r) Subsumed-by match: s Ascendant∈ (r) r Descenda∨ ∈

nt(s) Intersect match:

Fails

5.0max

nt (s)||) ||Ascendaant (r)||,(|| Ascend

(s)||Ascendant t (r)||Ascendan

Outline

Motivation Semantic Constraint for SWS Discovery Extracting Semantic Constraint Matching Algorithm Experiment Results Conclusions and Future Work

Experiment Results OWL-S TC v2:

576 web services from 7 domains 28 queries with their relevance sets. http://www-ags.dfki.uni-sb.de/ klusch/owls-mx/∼

Two sets of web services dataset1:

the semantic constraints of the output concepts in request and web service are manually annotated by two people

mainly described by service I/O concepts dataset2:

the semantic constraints of concepts are automatically extracted using the method represented above

Experiment Results [Klusch et al. 2006]

OWLS-M0 is a pure logic based matchmaker on the service I/O concepts

OWLS- M4 is reported to be the best-performing matchmaker variant of the OWLS-MX matchmaker

M0+InOutConstraint matchmaker uses CGM to filter the results of OWLS-M0 on Dataset1

M0+AutoConstraint matchmaker uses CGM to filter the results of OWLS-M0 on Dataset2

M4+InOutConstraint matchmaker uses CGM to filter the results of OWLS-M4 on Dataset1

M4+AutoConstraint matchmaker uses CGM to filter the results of OWLS-M4 on Dataset2

Experiment Results

The performance on Dataset1

InOutConstraint

OWLS-M4

OWLS-M0

Experiment Results

The performance on Dataset1

M4+InOutConstraint

OWLS-M4

OWLS-M0

M0+InOutConstraint

Experiment Results

The performance on Dataset2

M4+AutoConstraint

OWLS-M4

OWLS-M0

M0+AutoConstraint

AutoConstraint

Outline

Motivation Semantic Constraint for SWS Discovery Extracting Semantic Constraint Matching Algorithm Experiment Results Conclusions and Future Work

Conclusion Introduce semantic constraints for service I/O

concepts enhancing the semantics of web service

Extract semantic constraints automatically from the parsing trees of the description text

Use constraint graph to describe the semantic constraints of the service I/O concepts

A matching algorithm for the constraint graph

Future Work

Finding more effective extraction method to get better results of extraction

Extract more constraint relationships for the concepts web service can be represented by a more

complicated graphmore sophisticate matching algorithm

Thank you!