Conceptual similarity calculation using common-context between comparatives on ontology
- Authors
- Lee H.J.[Lee H.J.]; Sohn M.[Sohn M.]
- Issue Date
- 2014
- Keywords
- Cases; Common-Context; Component; Ontology; Similarity
- Citation
- Proceedings - 2014 8th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2014, pp.82 - 89
- Journal Title
- Proceedings - 2014 8th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2014
- Start Page
- 82
- End Page
- 89
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/57047
- DOI
- 10.1109/imis.2014.12
- Abstract
- 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. © 2014 IEEE.
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Collections - Engineering > Department of Systems Management Engineering > 1. Journal Articles
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