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

2. 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. 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. 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. 5 Integration  Resulting ontology can be composed of several “modules”  Be able to identify regions taken from different integrated ontologies

6. 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. 7 Use  Ontologies should be compatible among themselves  Issues for compatibility Ontological commitments Language Level of details Context etc.

8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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