Exploiting symmetry in relational similarity for ranking relational search results

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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 , where a word 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 . 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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages595-600
Number of pages6
Volume6230 LNAI
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010 - Daegu
Duration: 2010 Aug 302010 Sep 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6230 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010
CityDaegu
Period10/8/3010/9/2

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Exploiting symmetry in relational similarity for ranking relational search results'. Together they form a unique fingerprint.

Cite this