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A Review of Ontology Mapping, Merging, and Integration

A Review of Ontology Mapping, Merging, and Integration. Presenter: Yihong Ding. Survey Papers. Ontology Research and Development Part 2 – A review of Ontology Mapping and Evolving, Ying Ding and Schubert Foo

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A Review of Ontology Mapping, Merging, and Integration

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  1. A Review of Ontology Mapping, Merging, and Integration Presenter: Yihong Ding

  2. Survey Papers • Ontology Research and Development Part 2 – A review of Ontology Mapping and Evolving, Ying Ding and Schubert Foo • Some Issues on Ontology Integration, H. Sofia Pinto, A. Gomez-Perez, and Joao P. Martins

  3. Ontology Mapping • Two parties understand each other • Use the same formal representation • Share the conceptualization (so the same ontology) • Not easy to let everybody to agree on the same ontology for a domain • The problem of ontology mapping • Different ontologies on the same domain • Parties with different ontologies do not understand each other

  4. Ontology Integration • Building a new ontology and reusing other available ontologies (integration) • Merging different ontologies into a single one that “unifies” all of them (merging) • Integration of ontologies into applications (use)

  5. Integration • Resulting ontology can be composed of several “modules” • Be able to identify regions taken from different integrated ontologies

  6. Merging • Hard to identify regions taken from merged ontologies • Knowledge from merged ontologies is homogenized • Knowledge from one source ontology is scattered and mingled with the knowledge that comes from other sources

  7. Use • Ontologies should be compatible among themselves • Issues for compatibility • Ontological commitments • Language • Level of details • Context • etc.

  8. InfoSleuth’s reference ontology • Mapping • Explicit specified relationships of terms between ontologies • Encapsulated within resource agents • Resource agent • Encapsulate information about mapping rules • Present information in ontologies (reference ontologies) • Reference ontologies • Represented in OKBC • Stored in OKBC server • Ontology agents provide specifications • To users (for request formulation) • To resource agents (for mapping)

  9. Stanford’s ontology algebra • Mapping • Established articulations that enables the knowledge interoperability • Executed by ontology algebra • Ontology algebra • Operators • Unary: filter, extract • Binary: intersection, union, difference • Inputs: ontology graphs • Semi-automatic graph mapping • Domain experts define a variety of fuzzy matching • Use articulation ontology (abstract mathematical entities with some properties)

  10. AIFB’s formal concept analysis • Mapping and merging • Ontology concepts with the same extension • Executed by FCA-Merge • FCA-Merge • Create a concept hierarchy - the concept lattice -containing the original concepts based on the source ontologies • Process • Objects annotated by both ontologies: directly compute lattice • Else: create annotated objects first. • Else if cannot annotate: use documents as artificial objects. I.e., concepts which always appear in the same documents are supposed to be merged

  11. ECAI2000’s methods • Williams & Tsatsoulis • Supervised inductive learning • Create semantic concept descriptions • Apply concept clustering algorithm to find mapping • Tamma & Bench-Capon • Name-based matching • Relate classes in bottom-up and top-down ways • Priority functions to solve inconsistency • Human experts adjust priority functions • Uschold • Use a global reference ontology

  12. ISI’s OntoMorph • Syntactic rewriting • Pattern-directed rewrite rules • Concise specification of sentence-level transformations based on pattern matching • Semantic rewriting • Modulate syntactic rewriting via semantic models and logical inference

  13. KRAFT’s ontology clustering • Based on the similarities between the concepts known to different agents • Method • Use a domain ontology describe abstract information (global reference) • Each ontology cluster define certain part of its parent ontology • Name, instance, relation, compound matchers

  14. Heterogeneous Database Integration • A database scheme is a lightweight ontology • Typical researches • Batini et.al. (1986), five steps of integrating schemata of existing or proposed databases into a global, unified schema • Sheth & Kashyap (1992), semantic similarities in schema integration • Palopoli et.al. (2000), two techniques to integrate and abstract database schemes

  15. Other Ontology Mappings • Lehmann & Cohn (1994) • Need more specialized concept definitions • Li (1995) • Identify attribute similarities using neural networks • Borst & Akkermans (1997) • Resulted mappings could be considered as a new ontology

  16. Other Ontology Mappings • Hovy (1998) • Several heuristic rules to support the merging of ontologies • Weinstein & Birmingham (1999) • Graph mapping use description compatibility between elements • McGuinness et.al. (2000) • Chimaera system • Term merging from different knowledge sources • Noy & Musen (2000) • PROMPT algorithm for Protégé system • Ontology merging and alignment for OKBC compatible format

  17. Conclusion • Depend very much on the inputs of human experts • Focus on 1-1 mappings • Further needs n:1, 1:n, m:n mappings • Ontology mapping can be viewed as the projection of the general ontologies from different point of views

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