Improving relational search performance using relational symmetries and predictors

Tomokazu Goto, Nguyen Tuan Duc, Danushka Bollegala, Mitsuru Ishizuka

Research output: Contribution to journalArticle

Abstract

Relational similarity can be defined as the similarity between two semantic relations R and R' that exist respectively in two word pairs (A, B) and (C, D). Relational search, a novel search paradigm that is based on the relational similarity between word pairs, attempts to find a word D for the slot? in the query {(A,B), (C,?)} such that the relational similarity between the two word pairs (A, B) and (C, D) is a maximum. However, one problem frequently encountered by a Web-based relational search engine is that the inherent noise in Web text leads to incorrect measurement of relational similarity. To overcome this problem, we propose a method for verifying a relational search result that exploits the symmetric properties in proportional analogies. To verify a candidate result D for a query {(A, B), (C,?)}, we replace the original question mark by D to create a new query {(A,B),(?,D)} and verify that we can retrieve C as a candidate for the new query. The score of C in the new query can be seen as a complementary score of D because it reflects the reliability of D in the original query. Moreover, transformations of words in proportional analogies lead to relational symmetries that can be utilized to accurately measure the relational similarity between two semantic relations. For example, if the two word pairs (A, B) and (C, D) show a high degree of relational similarity then the two word pairs (B, A) and (D, C) also have a high degree of relational similarity. We apply this idea in relational search by using symmetric queries such as {(B, A), (D,?)} to create six queries for verifying a candidate answer D to improve the reliability of the verification process. Our experimental results on the Scholastic Aptitude Test (SAT) analogy benchmark show that the proposed method improves the accuracy of a relational search engine by a wide margin.

Original languageEnglish
Pages (from-to)649-656
Number of pages8
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume26
Issue number6
DOIs
Publication statusPublished - 2011
Externally publishedYes

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Search engines
World Wide Web
Semantics

Keywords

  • Proportional analogy
  • Relational search
  • Relational similarity
  • SAT

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Improving relational search performance using relational symmetries and predictors. / Goto, Tomokazu; Duc, Nguyen Tuan; Bollegala, Danushka; Ishizuka, Mitsuru.

In: Transactions of the Japanese Society for Artificial Intelligence, Vol. 26, No. 6, 2011, p. 649-656.

Research output: Contribution to journalArticle

Goto, Tomokazu ; Duc, Nguyen Tuan ; Bollegala, Danushka ; Ishizuka, Mitsuru. / Improving relational search performance using relational symmetries and predictors. In: Transactions of the Japanese Society for Artificial Intelligence. 2011 ; Vol. 26, No. 6. pp. 649-656.
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