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Ontology Mapping

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  • 1. A research on how to improve machine learning based ontology mapping Is Apple the Same as Orange? To: [email_address] Subject: Yu
  • 2. Presentation Overview
    • Semantic Web
    • Ontology
    • Ontology Mapping
      • Motivation
      • Methods (Machine Learning, Text Classification)
      • Problem
      • My Proposed Research
      • Evaluation
      • Current Results
      • Future Work
    • Comments & Questions
      • May mistaken something
      • EMAIL: yangyu1, UMBC
  • 3. The Semantic Web
    • “ in general, computers have no reliable way to process the semantics”
      • Some achievements by complicated algorithm (search engine)
      • Apple and orange: Apple is a kind of fruit ?Is there anther way?
    • Knowledge Base, Databases, standalone(?) structured information
      • HTML-Web, information not encoded, post-process
      • Database, information encoded, pre-process
    • Tim Berners-Lee, James Hendler, and Ora Lassila , 2001, the Semantic Web , Scientific American
    • "The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation."
  • 4. RDF -- well-defined meaning
    • “ uses URIs to encode information”,
    • “ the URIs ensure that concepts are not just words in a document but are tied to a unique definition that everyone can find on the Web”. (quoted from The Semantic Web)
    • Example:
      • http://www.amk.ca/talks/2003-03/
  • 5. RDF Example <rdf:RDF xmlns:FOAF=&quot;http://xmlns.com/foaf/0.1/&quot; xmlns:dc=&quot;http://purl.org/dc/elements/1.1/&quot; xmlns:rdf=&quot;http://www.w3.org/1999/02/22-rdf-syntax-ns#&quot; xmlns:rev=&quot;http://amk.ca/xml/review/1.0#&quot;> <!-- Implies rdf:type property is rev:Review --> <rev:Review rdf:about=&quot;http://example.com/rev1&quot;> <rev:subject rdf:resource=&quot;urn:isbn:1930110111&quot;/> </rev:Review> <rdf:Description rdf:about=&quot;http://example.com/author/0042&quot;> <FOAF:firstName>Bob</FOAF:firstName> <FOAF:homepage rdf:resource=&quot;http://www.snee.com/bob/&quot;/> <FOAF:pastProject rdf:resource=&quot;urn:isbn:1930110111&quot;/> <FOAF:surname>DuCharme</FOAF:surname> </rdf:Description> </rdf:RDF>
  • 6. RDF Example Description of the Author Even the Author’s Name is Apple, X well-defined meaning
  • 7. Ontology
    • What it is?
      • “Short answer: an ontology is a specification of a conceptualization”
        • http://www-ksl.stanford.edu/kst/ what-is-an-ontology .html
      • “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
      • Wine Ontology
      • http://www.w3.org/TR/owl-guide/ wine .rdf
    • How to use?
      • More sophisticated computing services will be based on Ontology
  • 8. Some Large Ontologies
    • OpenCyc (www.opencyc.org)
      • 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
    • WordNet (wordnet.princeton.edu)
      • English nouns synonym sets, verbs synsets, adjectives synsets and adverbs synsets each representing one underlying lexical concept. Different relations link the synonym sets.
    • OBO(obo.sourceforge.net)
      • 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.
    • SUMO (IEEE)
      • Suggested Upper Merged Ontology
      • General-purpose concepts, foundation for more specific ontologies for different domains.
  • 9. More ontologies
    • www.google.com/search?q=filetype:owl+owl
    • UMBC Swoogle (swoogle.umbc.edu)
    • My Question: How to use ontologies, still in research?
  • 10. 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
  • 11. Ontology Mapping
    • 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
  • 12. Manual Mapping
    • OpenCyc
      • 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
    • SUMO  WordNet
      • Mapping WordNet to the SUMO Ontology , Teknowledge Corp, 2002
    • Advantages and Disadvantages
  • 13. 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
  • 14. Machine Learning Based Approach
    • Machine Learning
      • 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)
    • Text Classification
      • Supervised Machine Learning
      • single-category text classification
  • 15. Some Machine Learning Based Ontology Mapping System
    • CAIMEN
      • 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
    • Glue
      • Doan Anhai, et al. 2003. Learning to match ontologies on the Semantic Web . Volume 12, Issue 4, VLDB Journal
  • 16. UMBC OntoMapper
    • 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
  • 17. 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.
  • 18. My Proposed Research
    • Obtaining Samplers from the Web Automatically
    • for Machine Learning Based Ontology Mapping
    • Advantages:
      • Ensure samplers quantity
        • Web Documents: A lot of Documents created in a distributed environment, well representing various aspects of a concept.
      • Low cost
        • By using search engines like Google, documents can be easily collected
    • Disadvantages:
      • Quality issue
  • 19. System Overview Samplers By Classes Samplers By Classes Queries A 1 Queries A 2 Ontology A 1 Ontology A 2 parser
  • 20. System Overview (Cont.) Model A 1 Model A 2 1 1 2 2 Samplers For A 1 Samplers For A 2 Text Classifier
  • 21. 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
  • 22. Evaluation
    • Compare the mapping results of the “enhanced” system with mapping results obtained from human experts.
  • 23. 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
  • 24. Questions & Comments