Efficient fuzzy ranking for keyword search on graphs

Nidhi R. Arora, Wookey Lee, Carson Kai Sang Leung, Jinho Kim, Harshit Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

When compared with the traditional single-node results returned by search engines, keyword search over graphs is a new answering paradigm that brings new challenges to ranking. In this paper, we propose an efficient fuzzy-set theory based ranking measure called FRank. This measure captures the presence and relevance of query keywords and their query-dependent edge weights. It evaluates the query answer based on the distribution of keywords in the query and the structural connectivity between these keywords. Experimental results show that our proposed FRank measure led to superior performance when compared with traditional ranking measures.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 23rd International Conference, DEXA 2012, Proceedings
Pages502-510
Number of pages9
EditionPART 1
DOIs
StatePublished - 2012
Event23rd International Conference on Database and Expert Systems Applications, DEXA 2012 - Vienna, Austria
Duration: 3 Sep 20126 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7446 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Database and Expert Systems Applications, DEXA 2012
Country/TerritoryAustria
CityVienna
Period3/09/126/09/12

Keywords

  • Fuzzy sets
  • graph rank
  • information retrieval (IR)
  • keyword search

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