An Extensible Framework for Generating Ontology from Various Data Models.

详细信息   

• 作者：Albarrak ; Khalid.
• 学历：Ph.D.
• 年：2013
• 导师：Sibley, Edgar H.,eadvisorBrodsky, Alexanderecommittee memberSchum, David A.ecommittee memberLaskey, Kathryn B.ecommittee memberBoicu, Mihaiecommittee member
• 毕业院校：George Mason University
• Department：Information Technology
• ISBN：9781303313851
• CBH：3591012
• Country：USA
• 语种：English
• FileSize：1357144
• Pages：269

文摘

In the Information Technology field, Ontology is concerned with the use of formal representation to describe concepts and relationships in a domain of knowledge. Using ontologies, organizations can facilitate processes such as integrating heterogeneous systems, assessing data quality, validating business rules, and discovering hidden facts. Ontology engineering, however, is not a trivial process. Developing ontologies is highly dependent on the availability and knowledge of ontology modelers and domain experts. Moreover, the development process is often lengthy and error-prone. In this dissertation, I developed an extensible framework for generating ontologies from data models. For this dissertation, the framework is limited to generating ontology from two types of data models: the Relational Database RDB) and Object-Relational Database ORDB) models. The framework, however, is extensible to support the generation of ontologies from other types of data models e.g. XML). The derived ontology is expressed in the OWL Web Ontology Language, a W3C recommendation. For RDB and ORDB models, my framework extracts information about these models from the metadata maintained by the Database Management System DBMS), and from the data instances in certain cases. The extracted metadata includes the integrity constraints that are typically maintained by a DBMS e.g. primary/foreign keys, not-null and unique constraints). In order to obtain more semantics from a data model implementation, the framework also examines data instances to discover some of the semantic gaps found in the metadata. Once extracted, the metadata and data instances are then analyzed to identify classes and their properties, discover explicit and implicit relationships between classes including potential class hierarchies), and identify restrictions related to properties and relationships. This analysis is based on heuristic database modeling techniques. The analyzed data model is then translated automatically into an OWL ontology that can be reviewed and/or augmented further with more semantics by ontology modelers based on input from domain experts. The proposed framework has been validated by implementing it as a prototype, and by examining the ontologies it generates from a syntactic and semantic perspective. For the semantic examination, domain requirements were used to compute the recall and precision for the ontologies generated by my framework and that of a similar tool. Moreover, the relative amount of terminological content which I call the relative explicitness) of these ontologies was measured as well using a methodology that I developed in my research. The results showed the ability of my framework to generate ontologies that are closely aligned with the domain.