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Viewpoint-Based Similarity Discernment on SNAP
Takashi YUKAWA Sanda M. HARABAGIU Dan I. MOLDOVAN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1999/02/25
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Artificial Intelligence and Cognitive Science
semantic network, marker propagation, analogy, inference system, parallel processing, natural language processing,
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This paper presents an algorithm for viewpoint-based similarity discernment of linguistic concepts on Semantic Network Array Processor (SNAP). The viewpoint-based similarity discernment plays a key role in retrieving similar propositions. This is useful for advanced knowledge processing areas such as analogical reasoning and case-based reasoning. The algorithm assumes that a knowledge base is constructed for SNAP, based on information acquired from the WordNet linguistic database. The algorithm identifies paths on the knowledge base between each given concept and a given viewpoint concept, then computes a similarity degree between the two concepts based on the number of nodes shared by the paths. A small scale knowledge base was constructed and an experiment was conducted on a SNAP simulator that demonstrated the feasibility of this algorithm. Because of SNAP's scalability, the algorithm is expected to work similarly on a large scale knowledge base.