The goal of the paper is to define requirements to OWL 2 ontologies, under which their semantics may be preserved in a relational database, and to demonstrate that the hybrid approach for transforming OWL 2 ontologies into relational databases possesses such capability. The hybrid approach maps part of ontology concepts to relational database concepts on the base of their common semantics; ontology constructs having no direct equivalents in databases are stored in metatables. The paper defines requirements for ontologies under transformation as ontology normalization and integrity rules, and presents a set of SQL queries for extracting rich data, covering semantics of source ontology, from the resulting database. The capability of the hybrid approach to preserve semantics of OWL 2 ontologies in relational databases is demonstrated with a representative example of a Vehicle ontology.
The goal of the paper is to define reversible, information preserving transformation from OWL 2 ontologies into relational databases using our proposed hybrid approach, when a part of ontology constructs is directly represented by relational database structures and another part having no direct correspondences in a relational database is stored in metadata tables. The desirable transformation is defined in QVT Relations language following additional requirements under which such transformation is reversible and does not lose semantic information when performing from ontology to database and backward.
This article discusses the process of enterprise knowledge extraction from relational database and source code of legacy information systems. Problems of legacy systems and main solutions for them are briefly described here. The uses of data reverse engineering and program understanding techniques to automatically infer as much as possible the schema and semantics of a legacy information system is analyzed. Eight step data reverse engineering algorithm for knowledge extraction from legacy systems is provided. A hypothetical example of knowledge extraction from legacy information system is presented.
Abstract. Semantics of Business Vocabulary and Business Rules (SBVR) is OMG adopted metamodel allowing defining noun concepts, verb concepts and business rules of a problem domain in structured natural language based on formal logics. SBVR business vocabulary and business rules are capable of representing ontologies. There are some research works devoted to transforming SBVR into Web Ontology Language OWL2. The reverse way of representing ontology concepts with SBVR structured language was not investigated though there are much more ontologies than SBVR vocabularies. Our research is concentrated on methodology for creating SBVR vocabularies and rules from OWL2 ontologies without a loss of the expressive power, characteristic for ontologies, as some ontology-specific concepts have no direct representation in SBVR. The particular attention is devoted to applying SBVR vocabulary in semantic search.
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