1 Semantic Search Agent System applying Semantic Web Techniques 2004.10.21 Jung-Jin Yang Intelligent Distributed Information System (I DIS) Lab. School of Computer Science & Information Engi neering The Catholic University of Korea [email protected]http://idis.catholic.ac.kr/jungjin
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Semantic Search Agent System applying Semantic Web Techniques
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. - PowerPoint PPT Presentation
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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
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
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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
• Ontology-based Semantic Search Agent
• 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