Ontology Mapping

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  • Ontology Mapping

    1. 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. 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. 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. 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. 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. 6. YANG YU (yangyu1, UMBC) RDF Example Description of the Author Even the Author’s Name is Apple, X well-defined meaning
    7. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. YANG YU (yangyu1, UMBC) System Overview Ontology A1 Ontology A2 parser Samplers By Classes Samplers By Classes Queries A1 Queries A2
    20. 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. 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. 22. YANG YU (yangyu1, UMBC) Evaluation  Compare the mapping results of the “enhanced” system with mapping results obtained from human experts.
    23. 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. 24. YANG YU (yangyu1, UMBC) Questions & Comments
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