A Framework and Methodology for Ontology Mediation through Semantic and Syntactic Mapping
| dc.contributor.author | Muthaiyah, Saravanan | |
| dc.creator | Muthaiyah, Saravanan | |
| dc.date | 2008-03-19 | |
| dc.date.accessioned | 2008-06-09T19:11:21Z | |
| dc.date.available | NO_RESTRICTION | |
| dc.date.available | 2008-06-09T19:11:21Z | |
| dc.date.issued | 2008-06-09T19:11:21Z | |
| dc.description.abstract | Ontology mediation is the process of establishing a common ground for interoperability between domain ontologies. Ontology mapping is the task of identifying concept and attribute correspondences between ontologies through a matching process. Ontology mediation and mapping enable ontologists to borrow and reuse rich schema definitions from existing domain ontologies that have already been developed by other ontologists. For example, a white wine distributor could maintain a white wine ontology that only has white wine concepts. This distributor may then decide at some point in the future to include other wine classifications as well in his current ontology. Instead of creating red wine or desert wine concepts in his existing ontology, the distributor could just borrow these concepts from existing red wine and desert wine ontologies. As such ontology mapping becomes necessary. The practice of matching ontology schemas today is one that is labor-intensive. Although semi-automated systems have been introduced, they are based on syntactic matching algorithms which do not produce reliable results. Thus my thesis statement is that the hybrid approach i.e., Semantic Relatedness Score (SRS), which combines both semantic and syntactic matching algorithms, provides better results in terms of greater reliability and precision when compared to pure syntactic matching algorithms. This research validates that SRS provides higher precision and relevance compared to syntactic matching techniques that have been used previously. SRS was developed through the process of rigorously testing thirteen well established matching algorithms and choosing a composite measure of the best combination of five out of those thirteen measures. This thesis also provides an end-to-end approach by providing a framework, process methodology and architecture for the process of ontology mediation. Since implementing a fully automated system without any human intervention would not be a realistic goal, a semi-automated approach is undertaken in this thesis. In this approach, an ontologist is assisted by a mapping system which selects the best candidates to be matched from the source and target ontology using SRS. The goal was not only to reduce the workload of the ontologist, but also provide results that are reliable. Literature survey on current ontology mediation research initiatives such as InfoSleuth, XMapper, ONION, FOAM, FCA-Merge, KRAFT, CHIMERA, PROMPT and OBSERVER, among others, revealed that the state-of-art of ontology mediation is to a large extent based on mainly syntactic schema matching that supported binary schema matches (1:1) only. A generic solution for schema matching based on SRS is presented in this thesis to overcome these limitations. A similarity matrix for concept similarity measures is introduced based on several cognitive and quantitative techniques such as computational linguistics, Latent Semantic Analysis (LSA), distance vectors and lexical databases (WordNet). The six-part matching algorithm is used to analyze RDF, OWL and XML schemas and to provide a similarity scores which are then used to populate a similarity matrix. The contribution here is twofold. Firstly, this approach gives a composite similarity metric and also supports complex mappings (1:n, 1:m, m:1 and n:m). Secondly, it provides higher relevance, reliability and precision. The validation of this approach is demonstrated by comparing SRS results with that of human domain experts. Empirical evidence provided in this document clearly shows that the hybrid method resulted in a higher correlation, better relevance and more reliable results than purely syntactic matching systems. Predefined Semantic Web Rule Language (SWRL) rules are also introduced to concatenate attributes, discover new relations and enforce the assertion box (ABox) instances. Reasoning for consistency, coherence, ontology classification and inference measures are also introduced. An actual implementation of this framework and process methodology for the mapping of security policy ontologies (SPRO) is provided as a case study. Another case study on achieving interoperability for e-government services with SWRL rules is also presented. Both SRS and SWRL rules are highlighted in this document as being complementary measures for the process of semantic bridging. Several tools were used for a proof-of-concept for the implementation of the methodology, including Protégé, Racer Pro, Rice and PROMPT. | |
| dc.identifier.uri | https://hdl.handle.net/1920/3070 | |
| dc.language.iso | en_US | |
| dc.subject | Ontology | |
| dc.subject | Mapping | |
| dc.subject | Mediation | |
| dc.subject | Semantic Web | |
| dc.subject | Semantic Web Rule Language | |
| dc.title | A Framework and Methodology for Ontology Mediation through Semantic and Syntactic Mapping | |
| dc.type | Dissertation | |
| thesis.degree.discipline | Information Technology | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy in Information Technology |