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1
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
3
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
…
4
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
7
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
8
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
10
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)
11
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
12
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
14
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
15
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
18
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.
20
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)
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
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
34
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]
36
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
37
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
38
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
39
SweetJess
is a new system for Semantic Web rules to be used in Jess
provides translation (DamlRuleML, RuleML, JessRule)
Provided by UMBC
40
UMLS
What’s it?
develops and distributes multi-purpose, electronic
"Knowledge Sources" and associated lexical
programs
41
OnSSAThe QueryModel
Ontology
Con
su
lting
Ag
en
t
GUI
MetaRule
SweetJess
Corpus-based (UMLS)
Concept-basedSearch
Eng
ine
Jena
Jes
s Rul
e
42
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
43
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
44
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
45
OnSSAInformation Agents
FindHumanGene
RelevantGene Score
RankOMIM#
MatchingPubMed ID
상태Make aQuery
HUGO OMIM GDB
LocusLink
Diseasename OMIM#
Scores
Reorderd OMIM#
46
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]).
}
48
Conclusion
Results of OnSSA in publications
Marriage of Semantic Web and Agent technology promising for more intelligent search strategy
49
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
50
Conclusion
Semantic Web + Web Service + Agent Technology
The real benefit is yet to come or already..