Bridging Real World Semantics to Model World Semantics for Taxonomy Based Knowledge Representation System

As a mean to map ontology concepts, a similarity technique is employed.Especially a context dependent concept mapping is tackled, which needscontextual information from knowledge taxonomy.Context-based semantic similarity differs from the real worldsimilarity in that it requires contextual information to calculatesimilarity. The notion of semantic coupling is introduced to derivesimilarity for a taxonomy-based system. The semantic coupling shows thedegree of semantic cohesiveness for a group of concepts toward a givencontext. In order to calculate the semantic coupling effectively,the edge counting method is revisited for measuring basic semanticsimilarity by considering the weighting attributes from where theyaffect an edge's strength. The attributes of scaling depth effect,semantic relation type, and virtual connection for the edge counting areconsidered. Furthermore, how the proposed edge counting method could bewell adapted for calculating context-based similarity is showed.Thorough experimental results are provided for both edge counting andcontext-based similarity. The results of proposed edge counting wereencouraging compared with other combined approaches, and thecontext-based similarity also showed understandable results. The novelcontributions of this paper come from two aspects. First, the similarityis increased to the viable level for edge counting. Second,a mechanism is provided to derive a context-based similarity intaxonomy-based system, which has emerged as a hot issue in theliterature such as Semantic Web, MDR, and other ontology-mappingenvironments.