As a mean to map ontology concepts, a similarity technique is employed.Especially a context dependent concept mapping is tackled, which needs contextual information fromknowledge taxonomy. Context-based semantic simil...As a mean to map ontology concepts, a similarity technique is employed.Especially a context dependent concept mapping is tackled, which needs contextual information fromknowledge taxonomy. Context-based semantic similarity differs from the real world similarity in thatit requires contextual information to calculate similarity. The notion of semantic coupling isintroduced to derive similarity for a taxonomy-based system. The semantic coupling shows the degreeof semantic cohesiveness for a group of concepts toward a given context. In order to calculate thesemantic coupling effectively, the edge counting method is revisited for measuring basic semanticsimilarity by considering the weighting attributes from where they affect an edge''s strength. Theattributes of scaling depth effect, semantic relation type, and virtual connection for the edgecounting are considered. Furthermore, how the proposed edge counting method could be well adaptedfor calculating context-based similarity is showed. Thorough experimental results are provided forboth edge counting and context-based similarity. The results of proposed edge counting wereencouraging compared with other combined approaches, and the context-based similarity also showedunderstandable results. The novel contributions of this paper come from two aspects. First, thesimilarity is increased to the viable level for edge counting. Second, a mechanism is provided toderive a context-based similarity in taxonomy-based system, which has emerged as a hot issue in theliterature such as Semantic Web, MDR, and other ontology-mapping environments.展开更多
文摘As a mean to map ontology concepts, a similarity technique is employed.Especially a context dependent concept mapping is tackled, which needs contextual information fromknowledge taxonomy. Context-based semantic similarity differs from the real world similarity in thatit requires contextual information to calculate similarity. The notion of semantic coupling isintroduced to derive similarity for a taxonomy-based system. The semantic coupling shows the degreeof semantic cohesiveness for a group of concepts toward a given context. In order to calculate thesemantic coupling effectively, the edge counting method is revisited for measuring basic semanticsimilarity by considering the weighting attributes from where they affect an edge''s strength. Theattributes of scaling depth effect, semantic relation type, and virtual connection for the edgecounting are considered. Furthermore, how the proposed edge counting method could be well adaptedfor calculating context-based similarity is showed. Thorough experimental results are provided forboth edge counting and context-based similarity. The results of proposed edge counting wereencouraging compared with other combined approaches, and the context-based similarity also showedunderstandable results. The novel contributions of this paper come from two aspects. First, thesimilarity is increased to the viable level for edge counting. Second, a mechanism is provided toderive a context-based similarity in taxonomy-based system, which has emerged as a hot issue in theliterature such as Semantic Web, MDR, and other ontology-mapping environments.
