1 / 16

Combining Declarative and Procedural Knowledge to Automate and Represent Ontology Mapping

Combining Declarative and Procedural Knowledge to Automate and Represent Ontology Mapping. Li Xu University of Arizona South. Ontology Mapping.

grazia
Download Presentation

Combining Declarative and Procedural Knowledge to Automate and Represent Ontology Mapping

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Combining Declarative and Procedural Knowledge to Automate and Represent Ontology Mapping Li Xu University of Arizona South

  2. Ontology Mapping • A process whereby two ontologies are semantically related at conceptual level, and the source ontology instances are transformed into the target ontology entities according to those semantic relations. • How to find matches? • How to represent mappings?

  3. About approaches to find matches • A key conclusion is that an effective approach to discover matches between ontology elements requires a principled combination of several base techniques e.g. • Ontology terms • Data types • Data values • Ontology structures • Domain knowledge is useful. • How to organize and represent the knowledge in an application domain?

  4. Knowledge Representation • Formal models of domain discourses • Keywords for vocabulary terms in ontologies • Metadata description about heterogeneous data between ontology instances

  5. About mapping representations • Mappings are for exchanging instances of a source ontology to instances of a target ontology. • Semantic interoperability is to use logic in order to guarantee that, after data are transmitted from a sender system to a receiver, all implications made by one system had to hold and be provable by the other, and that there should be a logical equivalence between those implications……from discussion in IEEE Standard Upper Ontology Working Group

  6. Declarative and Procedural Knowledge • Conceptualize domain knowledge into concepts and relationships between concepts. • Express logically equivalent concepts and relationships between ontologies. • Procedural attachment is used to enforce the expressive capability. • Information retrieval methods applied to retrieve keywords • Data extraction methods applied to match data with data patterns • Query computations issued by procedures that are beyond limitations of logic–based languages

  7. Domain Model Representation • Input Ontology O = (S, A, F) • Domain Ontology O = (S, A, P) • Source-to-Target Mapping Ontology O = (S, A, F, P)

  8. Year Car Car Target Ontology Signature Source Ontology Signature Input Ontologies Color Year Make Feature Feature Make & Model Model Body Type Style Miles Mileage Phone

  9. Domain Ontology Make & Model Feature Color Model Make Accessory Style Body Type Concepts and relationships Light weight ontologies Incremental development

  10. Describing Heterogeneous Metadata and data Make matches constant{ extract CarMakes case insensitive; }; lexicon{ CarMakes case insensitive; filename "carmakes.dict"; }; end; Mileage matches [8] constant{ extract "[1-9]\d{0,2}k" case insensitive; extract "[1-9]\d{0,2}?,\d{3}" case insensitive; extract "[1-9]\d{3,6}" case insensitive; … }; keyword "\bmiles\b", "\bmi\.", "\bmi\b", "\bmileage\b"; end;

  11. Make & Model Year Model Make Make & Model Year Make Feature Car Model Mileage Phone Finding Matches Color Feature Body Type Car Style Miles Source Evaluation

  12. Year Make Make & Model Model Year Make Feature Car Model Mileage Phone Source-to-Target Mapping Color Feature Body Type Car Style Miles Source Evaluation

  13. Feature Year Make & Model Color Accessory Style Body Type Feature’ Year Make Feature Car Model Mileage Phone Another Complex Match Color Feature Body Type Car Style Miles Source Evaluation

  14. Make Car Source Mapping Expressions s1.Car has Feature’ <= s1.Car has Make <= s1.Car has Model <= Model Feature’ Target Source-to-Target Mapping (Cont.) Year Year Make Feature Model Car Phone Mileage Miles Source

  15. Results • Experiments showed that domain ontologies provided a powerful method to explicitly represent heterogeneous metadata and data in order to support a composite approach for ontology mapping • Semantic interoperability based on a source-to-target mapping were proved to guarantee that extensional data transmitted from a source ontology can be interpreted correctly in a target ontology.

  16. Conclusions • Combining procedural and declarative representations provides a promising method to systematically and incrementally model domain knowledge to automate ontology mapping. • Combining procedural and declarative representation for mappings provides a semantic bridge to support semantic interoperability and to improve query and inference performance.

More Related