Transcript Semantic Web Ontology and Services

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 Korea
[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
…
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Semantic Search
An ontology introduces new possibilities for query/answering
Cooperative answering
I need info. about
deafness
DiseaseName(x)
and
gene(x,Caused)
Information
repository
Ontology
Tip:
There 30330 documents
for the desease, BUTonly
23 literatures with
relevant gene names
Semantic 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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User‘s information
need
Activities in
Searching for Information
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Query
Refinement
Relevant
resource exists
no
yes
It is found by
machine agent
yes
no
It is top-ranked
yes
no
User‘s request is
not satisfied
Information
repository
User has found a
resource relevant
for the query
Challenges
User‘s information
need
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- Query reflects the user’s need !
Query
- Information repository contains
resources relevant to the user’s need!
Relevant
resource exists
no
- Query
refinement
closes the gap
between the
query and the
user’s
information
need !
User‘s query is not
satisfied
yes
- Resources are annotated properly !
It is found by
software agent
no
yes
- Resources are ranked
according to the relevance
to the user‘s need !
It is top-ranked
yes
no
User has found a
resource relevant
for the query
Information
repository
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Agenda
• Semantic Search
• Ontology
• Ontology-based Semantic Search Agent
• OnSSA
• Conclusion
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Sementic Web Modeling
(figured by Jim Hendler at Semantic Web Conf. 2003)
…
Graph
Ontology
RDF
Labeled graph
Data Dictionary
Data Schema
Ontology
RDF Schema
Graph
+
limited logic
...
Ontology
OWL
Logic
Ontology
...
KIF?
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Ontology
Philosophy: 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.
Knowledge Representation
Representation 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 & Technologies
A 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
information and for accessing and reasoning with it in a
principled way.
Ontology query/update for ontology repositories
Ontology Modeling (Protégé 2000):
http://protege.stanford.edu
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Ontology Modeling (VOM):
http://www.sandsoft.com/
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Remark
Ontology
Standards
Integration: Semantic Integration
A language for writing data
Reaching out onto the Web
Ontology Modeling
No 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
• Ontology-based Semantic Search Agent
• OnSSA
• Conclusion
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Architecture of Intelligent Information Agent
An agent is anything that can be
viewed
as perceiving its environment
through sensors and acting upon
that environment through
effectors.
(by Russell & Norvig)
(by Enrico Franconi,
Univ. of Manchester, UK)
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Architecture of Intelligent Information Agent
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Architecture of Intelligent Information Agent
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Architecture of Intelligent Information Agent
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Architecture of Intelligent Information Agent
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Architecture of Intelligent Information Agent
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Architecture of Intelligent Information Agent
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Architecture of Intelligent Information Agent
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Architecture of Intelligent Information Agent
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Architecture of Intelligent Information Agent
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Architecture of Intelligent Information Agent
User
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Web
Service
UI Agent
Semantic IR System
Web Document
Translator
Search Engine
(Crawler Agent)
Inference Engine
RDF Query
Engine
Ontology Creator
Inference Rule
RDF
RDF Translator
OIL, DAML
Ontology
Evaluator
SHOE
Validator
Parser
Ontology
Validator
Ontology
Generator
Web Data
Repository
Ontology
Modeler
Database
Document
Editor
Ontology
Repository
Annotation
Tool
Ontology
Editor
Ontology/Web
Language
Ontology/Web
Language
Versioning
Tool
Ontology
Integration Tool
Ontology
Learner
Ontology
Integrator
Ontology
Selector
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Agenda
• Motivation
• Ontology
• Ontology-based Semantic Search Agent
• OnSSA
• Conclusion
OnSSA : Ontology-based Semantic
Search Agent
Requirements:
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
2. There are many types of related information and
represented in different forms.
=> Distributed information Agent
with different search strategies
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OnSSA
The System
User query
Query Models
Information
Agent 1
IR Agent
GUI
Query
Engine
Search engine
&
Ontology
PubMed
Consulting
Agent
Search/
Output
requery
Ranking
User
Information
Agent 2
OMIM
Information
Agent 3
Information
Agent 4
Result
Ranking
Mining
Engine
HUGO
Ensemble
Search Result
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OnSSA
Consulting 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 illdefined, user‘s information need [Saracevic75]
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QueryModel
is a concept-based rule engine
consist of Jena, SweetJess and Jess
Logic(Jess)
Translation(SweetJess)
Translation(Jena)
RuleML
Restrict(Jena) UMLS
ontology
RDF+rdfschema
XML+ns+xmlschema
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 manyto-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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OnSSA
The QueryModel
Corpus-based (UMLS)
GUI
Search Engine
Rule
SweetJess
Ontology
Jess
Jena
Concept-based
Consulting Agent
MetaRule
QueryModel Processing
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(reset)
②
(defrule rule1
(GeneDisease ?type ?query)
(UserInput ?query)
=>
(assert (Result ?query gene))
deafness
)
<?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>
GUI
</_opr>
(UserInterface)
<var>type</var>
<var>query</var>
</atom>
<atom>
QueryModel(reset)
<_opr>
(deffacts
(defrule
data(http://idis…
rule1…
(run)
MetaRule
<rel>UserInput</rel>
</_opr>
<var>query</var>
</atom>
Jena Jess
Rule
</and>
</_body>
<_head>
(deffacts data(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Total_transitory_deafness)
<atom>
(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Middle_ear_deafness)<_opr>
(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Bilateral_Deafness)
<rel>Result</rel>
(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Deafness_permanent_partial)
</_opr>
(http://idiscatholicackr/umlsRetrieveOtherRelation DEAFNESS Cockayne_Syndrome)
<var>query</var>
.
<ind>gene</ind>
.
</atom>
.
</_head>
(http://idiscatholicackr/umlsRetrieveOtherRelation DEAFNESS Lipreading)</imp>
(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Hearing_Loss_Sensorineural)
</rulebase>
(http://idiscatholicackr/umlsRetrieveBroader DEAFNESS Disability_NOS)
(UserInput DEAFNESS)
)
RuleML
Let’s Go!
①
SweetJess
UMLS
Search Engine
UMLS
Jena Semantic Web Toolkit
New fact & ReQuery
Ontology
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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 are from MEDLINE and additional life science journals.
OMIM
Online Mendelian Inheritance in Man is a database of human genes
and genetic disorders.
HUGO
Human gene nomenclature
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OnSSA
The System
User query
Query Models
Information
Agent 1
IR Agent
GUI
Query
Engine
Search engine
&
Ontology
PubMed
Consulting
Agnet
Search/
Output
requery
Ranking
User
Information
Agent 2
OMIM
Information
Agent 3
Information
Agent 4
Result
Ranking
Mining
Engine
HOGO
Ensemble
Search Result
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OnSSA
Information Agents
HUGO
Disease
name
Find
Human
Gene
Make a
상태
Query
OMIM
OMIM#
GDB
Relevant
Gene Score
Matching
PubMed ID
LocusLink
Scores
Rank
OMIM#
Reorderd
OMIM#
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OnSSA
Agent 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] <EXIST S 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
Web Service
Space
■
WS
WS
■
Application
Application
■
Other
Other
■
Agent
Agent
■Gateway
Gateway
■
Server
Server
■
Agent
Agent
Agent
Platform
Other
Agent
■
Server
ServerAPI
API
Ontology Repository
Other Agent
Platform
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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..