Exploiting symmetry in relational similarity for ranking relational search results

Authors:
Tomokazu Goto;Nguyen Tuan Duc;Danushka Bollegala;Mitsuru Ishizuka
Affiliations:
The University of Tokyo, Japan;The University of Tokyo, Japan;The University of Tokyo, Japan;The University of Tokyo, Japan
Venue:
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Year:
2010

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Abstract

Relational search is a novel paradigm of search which focuses on the similarity between semantic relations. Given three words (A, B, C) as the query, a relational search engine retrieves a ranked list of words D, where a word D ∈ D is assigned a high rank if the relation between A and B is highly similar to that between C and D. However, if C and D has numerous co-occurrences, then D is retrieved by existing relational search engines irrespective of the relation between A and B. To overcome this problem, we exploit the symmetry in relational similarity to rank the result set D. To evaluate the proposed ranking method, we use a benchmark dataset of Scholastic Aptitude Test (SAT) word analogy questions. Our experiments show that the proposed ranking method improves the accuracy in answering SAT word analogy questions, thereby demonstrating its usefulness in practical applications.