Research On Sub-Ontology Model For Large-Scale Ontology Reuse

As the knowledge representation foundation of the Semantic Web, ontologies play a critical role in building a large variety of semantic-based systems or applications. With the growth of ontologies, it will decrease the efficiency of systems in manipulating and using ontologies. However, a specific Semantic Web application often needs portions of a large-scale domain ontology. Considering the existing work about the Semantic Web and ontology, it still lacks of efficient models and methods to support reusing large-scale ontologies in Semantic Web applications. In order to construct and popularize applications towards the Semantic Web, how to manage and utilize large-scale ontologies has become a practical and urgent requirement.Under this background, we mainly talk about a sub-ontology model for large-scale ontology reuse in this thesis. The major research efforts and contributions are as follows:â–¡Considering the locality of Semantic Web applications in using ontology, we propose the representation of sub-ontology. Context-specific portions from large-scale ontology are represented as sub-ontologies. This thesis gives a formal definition of sub-ontology and defines a collection of object manipulations for sub-ontology. Semantic Web applications can extract sub-ontologies dynamically according to requirements and form specific sub-ontology knowledge-bases. This thesis also combines the caching mechanism with ontology reuse to form a sub-ontology cache, which is used as the local knowledge-base of semantic-based systems to support dynamic ontology reuse.â–¡This thesis also presents the research about the sub-ontology reasoning and illustrates the basic reasoning tasks of sub-ontology. The reasoning algorithm based on sub-ontology is used to reduce the reasoning problem for ontology into the one for sub-ontology. In this way, it improves the efficiency of reasoning by decreasing the complexity. A tableau algorithm based on sub-ontology representation is given to support modularized ontology reasoning. We also prove that the tableau algorithm for sub-ontology is semi-deterministic compared with that of ontology. An expansion reasoning algorithm is given for preserving consistency.â–¡Considering the problem of sub-ontology knowledge-base optimization, this thesis presents an optimization approach based on genetic algorithm. The approach extends the canonical genetic algorithm to form a semantic-based genetic algorithm called SemGA. The algorithm uses triple-based non-binary encoding to represent sub-ontologies as chromosomes and performs genetic operators based on semantic relations. SemGA can evolve sub-ontology cache dynamically to optimize the semantic structure of knowledge-base. Compared with the traditional cache policies, the evolution-based approach has benefits in both efficiency and performance.â–¡This thesis presents a sub-ontology based method for integrating and managing distributed Web resources. The method makes use of ontology semantics to integrate distributed Web resources. It manages resources in terms of sub-ontology by creating semantic mappings between the schemata of resources and ontology. The process of resource matching is transformed to the matching between resource requests and sub-ontologies. A genetic algorithm is used to achieve resource optimization to satisfy dynamic resource requirements. The result of simulation experiment illustrates that the algorithm improves the efficiency of resource matching and reuse.On the basis of the work before-mentioned, this thesis also presents a prototype system for sub-ontology called DartOnto. The system is used to support reusing large-scale ontology in the field of traditional Chinese medicine. We illustrate how to use the sub-ontology model in constructing traditional Chinese medicine knowledge service and solving the problem of reusing large-scale domain ontology through a use case.