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Ontology Mapping

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    • 1. YANG YU (yangyu1, UMBC) A research on how to improve machine learning based ontology mapping Is Apple the Same as Orange? To: nicholas@csee.umbc.edu Subject: Yu
    • 2. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) RDF Example <rdf:RDF xmlns:FOAF="http://xmlns.com/foaf/0.1/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rev="http://amk.ca/xml/review/1.0#"> <!-- Implies rdf:type property is rev:Review --> <rev:Review rdf:about="http://example.com/rev1"> <rev:subject rdf:resource="urn:isbn:1930110111"/> </rev:Review> <rdf:Description rdf:about="http://example.com/author/0042"> <FOAF:firstName>Bob</FOAF:firstName> <FOAF:homepage rdf:resource="http://www.snee.com/bob/"/> <FOAF:pastProject rdf:resource="urn:isbn:1930110111"/> <FOAF:surname>DuCharme</FOAF:surname> </rdf:Description> </rdf:RDF>
    • 6. YANG YU (yangyu1, UMBC) RDF Example Description of the Author Even the Author’s Name is Apple, X well-defined meaning
    • 7. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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 A1 Ontology A2 Obtaining probabilistic values (N * M) that shows how well class ni in Ontology A1 maps to class nj in Ontology A2 N M
    • 12. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) System Overview Ontology A1 Ontology A2 parser Samplers By Classes Samplers By Classes Queries A1 Queries A2
    • 20. YANG YU (yangyu1, UMBC) System Overview (Cont.) Samplers For A1 Samplers For A2 Model A1 Model A2 Text Classifier 1 1 2 2
    • 21. YANG YU (yangyu1, UMBC) Text Classifier System Overview (Cont.) Ontology A1 Model A2 Samplers For A1 Samplers for N classes Suppose having N classes models for M classes Obtaining probabilistic values (N * M) that shows how well class ni in Ontology A1 maps to class nj in Ontology A2 models for M classes
    • 22. YANG YU (yangyu1, UMBC) Evaluation  Compare the mapping results of the “enhanced” system with mapping results obtained from human experts.
    • 23. YANG YU (yangyu1, UMBC) 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. YANG YU (yangyu1, UMBC) Questions & Comments

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