Study On Inconsistency In Ontologies

As a clear expression of the semantic and knowledge sharing, ontology has become the core of the semantic Web. In recent years, research on ontology has been great progress. However, it is often difficult to construct an ontology which is error-free in practice. Inconsistency can occur due to several reasons, such as mis-presentation of defaults, polysemy, merging ontologies, migration from another formalism. The classical entailment in logics is explosive:any formula is a logical consequence of a contradiction. Conclusions drawn from an inconsistent ontology by classical inference may be completely meaningless. Therefore, studies on how to deal with inconsistent ontologies have become the academic focus of attention.This paper makes an overall analysis of the present situation in inconsistent ontology, and then mainly provide solutions to measuring inconsistency, diagnosing inconsistency and reasoning with inconsistency. Finally, an empirical analysis is made of the research models with forestry domain ontology constructed by us.(1) Measuring inconsistency. Firstly, this paper proposes an ontology inconsistency measures method named ETOIM and proves the correctness of the result. Secondly, a more effective selection function-based inconsistency measures method named SETOIM is presented. SETOIM improves ETOIM by using syntactic relevance selection functions, which is closer to the the "fact" than ETOIM. Compared with the typical similar algorithms in the field, the experimental results demonstrate our methods is effective.(2) Diagnosing and reparing inconsistency. We propose a more effective approach to diagnose and repair inconsistency than traditional local diagnosis. Our approach can exclude some "innocents" and some "condemners" from diagnosis range according to new evidence. Because the diagnosis range is narrowed twice, efficiency is increased significantly. Furthermore, our approach can avoid involving the innocent in the trouble by removing the "condemners", and the accuracy of diagnosis is improved.(3) Reasoning with inconsistency. Based on the linear extension reasoning algorithms, this paper proposes a framework of reasoning with inconsistent ontologies. The former is fit to the ontologies with the lower degree of inconsistency, and latter is fit to the ontologies with the higher degree of inconsistency. Both of the reasoning methods are only based on a syntactic relevance-based selection, which makes the inference space between two linear transformations larger. Hence, meaningful answers are easily losed. Both of the reasoning methods are referred to as the "coarse grained" reasoning. Accordingly, the "fine grained" reasoning based on the measure results is presented to improve the "coarse grained" reasoning, so as to increase the efficiency of reasoning.(4) Developing forestry knowledge resource management sevice system based on forestry ontology. The system consists of three parts:forestry domain ontology construction module, ontology inconsistent processing module and the semantic retrieval module. Our new approaches for dealing with inconsistencies are verified one by one in ontology inconsistent processing module.