“The most typical kind of ontology for the Web has a taxonomy and a set of inference rules”
From The Semantic Web
RDF, RDF-S, OWL ( www.w3c.org )
A sample ontology
http://www.w3.org/TR/owl-guide/ wine .rdf
How to use?
More sophisticated computing services will be based on Ontology
Some Large Ontologies
the world's largest and most complete general knowledge base and commonsense reasoning engine.
47,000 concepts: an upper ontology whose domain is all of human consensus reality, interrelated and constrained by 306,000 assertions
English nouns synonym sets, verbs synsets, adjectives synsets and adverbs synsets each representing one underlying lexical concept. Different relations link the synonym sets.
Open Biomedical Ontology project Supported by NIH, NSF, etc.
Biological and medical domains, Sequnce, Palnt, etc. Eg, Gene Ontology: 17746 terms, 93.9% with definitions.
Suggested Upper Merged Ontology
General-purpose concepts, foundation for more specific ontologies for different domains.
UMBC Swoogle (swoogle.umbc.edu)
My Question: How to use ontologies, still in research?
Why Ontology Mapping
The same term in two ontologies may mean different (previous example).
Different Organizations may use different ontologies for the same domain, resulting different terms representing the same concept (eg, AI & CI); problems arise when they try to communicate with each other – “ interoperability problem ”
H. S. Pinto. 1999, Some issues on ontology integration. In IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5)
Hi, I want to buy some apples . What are you talking about? I only sell Red and Delicious
Try to find relationships between each pair of concepts used in two different ontologies. For example, Equivalent, Subclass_Of, Superclass_Of, Siblings, Similar (how much similar?), Different (how much different?)
Ontology A 1 Ontology A 2 Obtaining probabilistic values (N * M) that shows how well class n i in Ontology A 1 maps to class n j in Ontology A 2 N M
SENSUS, FIPS 10-4,several large (300k-term) pharmaceutical thesauri, large portions of WordNet, MeSH/Snomed/UMLS, and the CIA World Factbook.
Knowledge worker + domain expert
Interactive clarification tool + domain expert
Mapping Ontologies into Cyc , Cyc Corp, 2002
Mapping WordNet to the SUMO Ontology , Teknowledge Corp, 2002
Advantages and Disadvantages
Lexical Based Approach
John Li, 2003, LOM – a Lexicon based ontology mapping tool. Information Interpretation and Integration Conference
String matching, adding some techniques, like word stem
MeetingPlace and the_Place_of_Meeting
Write and Written
Machine Learning Based Approach
Learning is a process, after which, if success, enables one to do something one cannot do before.
“ Machine learning refers to a system capable of the autonomous acquisition and integration of knowledge” (AAAI)
Supervised Machine Learning
single-category text classification
Some Machine Learning Based Ontology Mapping System
Lacher, M.; and Groh, G. May 2001. Facilitating the Exchange of Explicit Knowledge through Ontology Mappings . In Proceedings of the 14th International FLAIRS Conference. Key West, FL, USA
Doan Anhai, et al. 2003. Learning to match ontologies on the Semantic Web . Volume 12, Issue 4, VLDB Journal
Prasad, S.; Peng, Y.; and Finin, T. 2002. A Tool For Mapping Between Two Ontologies (Poster), International Semantic Web Conference ( ISWC02 ).
According to the researchers: Results not encouraging because of very few samplers
A Problem of Machine Learning Based Ontology Mapping
Samplers used to train the learner are collected or created manually by ontology workers
May ensure quality?
Lack of quantity
If samplers are not enough, a concept may not be well represented.
My Proposed Research
Obtaining Samplers from the Web Automatically
for Machine Learning Based Ontology Mapping
Ensure samplers quantity
Web Documents: A lot of Documents created in a distributed environment, well representing various aspects of a concept.
By using search engines like Google, documents can be easily collected
System Overview Samplers By Classes Samplers By Classes Queries A 1 Queries A 2 Ontology A 1 Ontology A 2 parser
System Overview (Cont.) Model A 1 Model A 2 1 1 2 2 Samplers For A 1 Samplers For A 2 Text Classifier
System Overview (Cont.) Model A 2 Samplers For A 1 Samplers for N classes Suppose having N classes models for M classes Obtaining probabilistic values (N * M) that shows how well class n i in Ontology A 1 maps to class n j in Ontology A 2 models for M classes Text Classifier Ontology A 1
Compare the mapping results of the “enhanced” system with mapping results obtained from human experts.
Current Result & Future Work
Text Classifier Rainbow doesn’t work well, considering switching to other text classification tool, for example Weka or some sourceforge projects.
Trying to find how to utilize the raw probabilistic value obtained from the cross-classification.
Trying to use clustering algorithms to improve the quality of the samplers