Semantic Search Agent System applying Semantic Web Techniques

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Semantic Search Agent System applying Semantic Web Techniques

2004.10.21

Jung-Jin Yang

Intelligent Distributed Information System (IDIS) Lab.

School of Computer Science & Information Engineering

The Catholic University of [email protected]

http://idis.catholic.ac.kr/jungjin

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Agenda

• Semantic Search

• Ontology

• Ontology-based Semantic Search Agent

• OnSSA

• Conclusion

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Searching Semantically

How to handle problems in searching for information?

Time intensive

e.g. for the query “disease and remedy” a user cannot find a relevant result

What can be the problem:

1. the query is too ambiguous

2. the used terms do not match the repository

3. the results are not properly ranked

…

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Moreover

Cognitive demand on users in a professional domain

e.g. for the query “hearing deficit” in searching medical literature through MEDLINE DB a user cannot find adequate results

What can be the problem:

1. the query is too ambiguous

2. the used terms do not match the repository

3. the results are not properly ranked

4. the lacking knowledge of professional terms

…

5Semantic Search

Information repositoryI need info. about

deafness

Tip:There 30330 documents for the desease, BUTonly 23 literatures with relevant gene names

Ontology

An ontology introduces new possibilities for query/answeringCooperative answering

DiseaseName(x) and gene(x,Caused)

6Semantic Search

Develop an intelligent agent system to produce a more precise search result

combine search engine and ontology

corpus-based & concept-based

supports continual improvement of an information retrieval according to its usage

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It is found by machine agent

yes

Relevant resource exists

Activities in Searching for Information

User‘s information need

Query

yes

It is top-ranked

User has found a resource relevant

for the query

yes

User‘s request is not satisfied

no

no

no

Ref

inem

ent

Information repository

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Relevant resource exists

It is found by software agent

- Information repository contains resources relevant to the user’s need!

- Resources are annotated properly !

User has found a resource relevant

for the query

yes

yes

no

no

Query

User‘s query is not satisfied

ChallengesUser‘s information need

It is top-ranked

- Query reflects the user’s need !

- Resources are ranked according to the relevance to the user‘s need !

yes

no

- Query refinement closes the gap between the query and the user’s information need !

Information repository

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Agenda

• Semantic Search

• Ontology


• OnSSA

• Conclusion

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Sementic Web Modeling

RDF RDF Schema OWL

Graph Labeled graph

Ontology

Data DictionaryData Schema

…

...

... Logic

KIF?

OntologyOntology Ontology

Graph +

limited logic

(figured by Jim Hendler at Semantic Web Conf. 2003)

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OntologyPhilosophy: A systematic account of existence

An ontology is a formal conceptualization of the world. (T. R. Gruber)

An ontology specifies a set of constraints, which declare what should necessarily hold in any possible world.

An ontological commitment is an agreement to use a vocabulary (i.e., ask queries and make assertions) in a way that is consistent (but not complete) with respect to the theory specified by an ontology: Knowledge Sharing

An ontology specifies a rich description of the :Terminology

Concepts

Relationships between the concepts

Rules

Relevant to a particular domain or area of interest

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Upper-, Mid-level, Lower-Ontologies

An upper-ontology defines very broad, universal Classes and properties

Example: Cyc Upper Ontology

http://www.opencyc.org

A mid-level ontology is an upper ontology for a specific domain

A lower-ontology is an ontology for a specific domain, with specific Classes and properties.

You can merge into an umbrella, upper-level ontology by defining your ontologies root class as a subClassOf a class in the upper-ontology.

13Knowledge RepresentationRepresentation of knowledge

Description of world of interests

Usable by machines to make conclusions about that world

Intelligent System

Computational system

Uses an explicitly represented store of knowledge

To reason about its goals, environment, other agents, itself

Expressiveness vs. tractability tradeoff

How to express what we know

How to reason with what we express

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Processing Knowledge = “Reasoning”

Representation of Knowledge

Access represented knowledge and process it.

Access alone is, in general, insufficient

Implicit knowledge has to be made explicit

deduction methods

The results should only depend on the semantics …

And not on accidental syntactic differences in representations

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Ontology Modeling & TechnologiesA systematic account of existence of knowledge and intelligence for a particular domain

Ontology modeling using appropriate Tools and Language

e.g., OntoEdit, OilEd, Protégé, VOM (Visual Ontology Modeler)

e.g., XML, RDF, OWL

Reasoning capabilities: Description Logics

Provide theories and systems for expressing structured inform

ation and for accessing and reasoning with it in a principled w

ay.

Ontology query/update for ontology repositories

16Ontology Modeling (Protégé 2000):http://protege.stanford.edu

17Ontology Modeling (VOM):http://www.sandsoft.com/

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Remark

OntologyStandards

Integration: Semantic Integration

A language for writing data

Reaching out onto the Web

Ontology ModelingNo one correct way to model a domain

Iterative ontology development process

Natural correspondence to objects and relationships in

your domain of interest.

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Agenda

• Semantic Search

• Ontology


• OnSSA

• Conclusion

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Architecture of Intelligent Information Agent

(by Enrico Franconi,

Univ. of Manchester, UK)

An agent is anything that can be viewed

as perceiving its environment

through sensors and acting upon

that environment through effectors. (by Russell & Norvig)

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28


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31User UI Agent

WebService

Search Engine(Crawler Agent)

Inference Engine Inference Rule

RDF Query Engine

Ontology Creator

Document Editor

Ontology Repository

ParserValidator

Ontology Evaluator

VersioningTool

OntologyValidator

OntologyGenerator

OntologyModeler Database

Web DataRepository

AnnotationTool

OntologyEditor

Ontology Integration Tool

Ontology/WebLanguage

Ontology/WebLanguage

OntologySelector

OntologyLearner

OntologyIntegrator

Web Document

Translator

RDF

RDF Translator

OIL, DAML

SHOE

Semantic IR System

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Agenda

• Motivation

• Ontology


• OnSSA

• Conclusion

33OnSSA : Ontology-based Semantic Search Agent

1. Users are reluctant/unable to provide explicit feedback about the „quality“ of the ontology

=> use implicit relevance feedback

suggested lists of broader/narrower terms

Requirements:

2. There are many types of related information and represented in different forms.

=> Distributed information Agent

with different search strategies

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OnSSAThe System

requery

IR Agent

QueryEngine

Search/Output

Ranking

InformationAgent 1

Search engine&

Ontology

Query Models

PubMed

OMIM

HUGO

Ensemble

MiningEngine

User query

ResultRanking

Search Result

ConsultingAgent

GUI

User

InformationAgent 2

InformationAgent 3

InformationAgent 4

35OnSSA

Consulting AgentConsulting Agent

1. Query Refinement

2. Ranking Management

Query management:

What is a user searching for?

Note:A user‘s query is just an approximation of the, often ill-defined, user‘s information need [Saracevic75]

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QueryModel

is a concept-based rule engine

consist of Jena, SweetJess and Jess

ontology

Translation(Jena)Translation(SweetJess)

Logic(Jess)

RDF+rdfschema

XML+ns+xmlschema

Restrict(Jena)RuleML UMLS

QueryModels Architecture

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Jena

Store a data of RDF and represent RDF graphs and write in N-Triples format

Load a Daml+OIL ontology in Java using Jena

Navigate an RDF graph within Jena using RDQL

Jena Architecture

RDQL Grammar

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Jess

is a rule engine and scripting environment written entirely in JAVA

uses the Rete algorithm to process rules, a very efficient mechanism for solving the difficult many-to-many matching problem

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SweetJess

is a new system for Semantic Web rules to be used in Jess

provides translation (DamlRuleML, RuleML, JessRule)

Provided by UMBC

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UMLS

What’s it?

develops and distributes multi-purpose, electronic

"Knowledge Sources" and associated lexical

programs

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OnSSAThe QueryModel

Ontology

Con

su

lting

Ag

en

t

GUI

MetaRule

SweetJess

Corpus-based (UMLS)

Concept-basedSearch

Eng

ine

Jena

Jes

s Rul

e

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Let’s Go!

GUI(UserInterface)

UMLS

Ontology

QueryModelMetaRule

RuleJess

SweetJess

Jena

UM

LS

Search Engine

deafness

Jena Semantic Web Toolkit(deffacts data(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Total_transitory_deafness)(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Middle_ear_deafness)(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Bilateral_Deafness)(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Deafness_permanent_partial)(http://idiscatholicackr/umlsRetrieveOtherRelation DEAFNESS Cockayne_Syndrome)

.

.

.(http://idiscatholicackr/umlsRetrieveOtherRelation DEAFNESS Lipreading)(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Hearing_Loss_Sensorineural)(http://idiscatholicackr/umlsRetrieveBroader DEAFNESS Disability_NOS)(UserInput DEAFNESS))

①

GUI(UserInterface)

UMLS

Ontology

QueryModelMetaRule

RuleJess

SweetJess

Jena

UM

LS

Search Engine

<?xml version="1.0" encoding="UTF-8"?><rulebase xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="http://userpages.umbc.edu/~mgandh1/2002/06/RuleML/ruleml-sclp-prag-v13.xsd" direction="forward"> <imp> <_rlab> <ind>rule1</ind> </_rlab> <_body> <and> <atom> <_opr> <rel>GeneDisease</rel> </_opr> <var>type</var> <var>query</var> </atom> <atom> <_opr> <rel>UserInput</rel> </_opr> <var>query</var> </atom> </and> </_body> <_head> <atom> <_opr> <rel>Result</rel> </_opr> <var>query</var> <ind>gene</ind> </atom> </_head> </imp></rulebase>

RuleML

(reset)

(defrule rule1(GeneDisease ?type ?query)(UserInput ?query)=>(assert (Result ?query gene)))

②

GUI(UserInterface)

UMLS

Ontology

QueryModelMetaRule

RuleJess

SweetJess

Jena

UM

LS

Search Engine

(deffacts data(http://idis…(reset) (defrule rule1…(run)

New fact & ReQuery

QueryModel Processing

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Introduction about Databases

MEDLINE

A database of indexed journal citations and abstracts.

Pubmed

a service of the National Library of Medicine, includes over 14 million

citations for biomedical articles back to the 1950's. These citations a

re from MEDLINE and additional life science journals.

OMIM

Online Mendelian Inheritance in Man is a database of human genes a

nd genetic disorders.

HUGO

Human gene nomenclature

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OnSSA

The System

requery

IR Agent

QueryEngine

Search/Output

Ranking

InformationAgent 1

Search engine&

Ontology

Query Models

PubMed

OMIM

HOGO

Ensemble

MiningEngine

User query

ResultRanking

Search Result

ConsultingAgnet

GUI

User

InformationAgent 2

InformationAgent 3

InformationAgent 4

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OnSSAInformation Agents

FindHumanGene

RelevantGene Score

RankOMIM#

MatchingPubMed ID

상태Make aQuery

HUGO OMIM GDB

LocusLink

Diseasename OMIM#

Scores

Reorderd OMIM#

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OnSSAAgent Ontology

daml := 'http://www.daml.org/.../daml+oil#'.localAgent := 'http://localhost/localAgent#'.@ localAgent:ontology { localAgent:Disease[rdf:type - > daml:Class]. localAgent:Gene[rdf:type - > daml:Class; rdfs:subClassOf - > localAgent:Disease]. localAgent:General[rdf:type - > daml:Class; rdfs:subClassOf - > localAgent:Disease; daml:disjointWith - > localAgent:Gene].localAgent:Human[rdf:type - > daml:Class; rdfs:subClassOf - > localAgent:Gene].localAgent:Animal[rdf:type - > daml:Class; rdfs:subClassOf - > localAgent:Gene;daml:disjointWith - > localAgent:Human].FORALL Mdl @rdfschema(Mdl){ //model block

FORALL O,P,V O[P- >V] <- O[P- >V] @Mdl. // copy triples from Mdl

…FORALL O,P,V O[subClassOf - > V] <-EXISTS W (O[subClassOf - > W] AND W[subClassOf - > V]).

}

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Agenda

• Semantic Search

• Ontology

• OnSSA

• Conclusion

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Conclusion

Results of OnSSA in publications

Marriage of Semantic Web and Agent technology promising for more intelligent search strategy

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Future: Agent-based Service Ontology Structure

■Server API

■Server■Agent

OtherAgent

■Other■Agent

AgentPlatform

Other Agent Platform

Web ServiceSpace

■Gateway

■WS■Application

Server API

ServerAgent

OtherAgent

Gateway

Ontology Repository

WSApplication

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Conclusion

Semantic Web + Web Service + Agent Technology

The real benefit is yet to come or already..

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Thank You..

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Semantic Search Agent System applying Semantic Web Techniques