Conceptual Similarity Calculation Using Common-Context between Comparatives on Ontology

For effective searching of appropriate information, it is necessary to well organize data to access and store in database. So, we adopt a case structure as a formalized data form. Web resources are transformed into cases which help information processing and accessing. In addition, we define common-context which are shared concepts by comparatives and propose a common-context-based conceptual similarity through arc compression on ontology. Arc-based conceptual distance between comparative nodes is calculated under consideration of common-context. One of comparatives comes from the user requirements and another from indexes of a case. The distance is contingent upon consideration of common-context. The 'Node Compression (NC)' and 'Arc Compression (AC)' are proposed to support the dynamicity of similarity. NC is conducted between adjacent common-context nodes and leads calculation of conceptual distance between comparatives. AC is processed between non-adjacent common-context nodes. The conceptual arc compression is conducted by Weighted Partial Ontology (WPO) based on weights of arcs under consideration of common-context. The proposed NC and AC support to return conceptual distance between comparatives because it increases the concept-based reliability of search result. To verify the effectiveness, the proposed conceptual similarity is compared with that of edge-counting similarity method. We show that the proposed conceptual similarity calculation leads a higher similarity value for conceptually close classes compared with other methods.