Diversifying the results of keyword queries on linked data

Ananya Dass, Cem Aksoy, Aggeliki Dimitriou, Dimitri Theodoratos, Xiaoying Wu

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

5 Scopus citations


Keyword search is a popular technique for retrieving information from the ever growing repositories of RDF graph data on the Web. However,keyword queries are inherently ambiguous,resulting in an overwhelming number of candidate results. These results correspond to different interpretations of the query. Most of the current keyword search approaches ignore the diversity of the result interpretations and might fail to provide a broad overview of the query aspects to the users who are interested in exploratory search. To address this issue,we introduce in this paper,a novel technique for diversifying keyword search results on RDF graph data. We generate pattern graphs which are structured queries corresponding to alternative interpretations of the given keyword query. We model the problem as an optimization problem aiming at selecting a set of k pattern graphs with maximum diversity. We devise a metric to estimate the diversity of a set of pattern graphs,and we design an algorithm that employs a greedy heuristic to generate a diverse list of k pattern graphs for a given keyword query.

Original languageEnglish (US)
Title of host publicationWeb Information Systems Engineering – WISE 2016 - 17th International Conference, Proceedings
EditorsWojciech Cellary, Jianmin Wang, Mohamed F. Mokbel, Hua Wang, Rui Zhou, Yanchun Zhang
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319487397
StatePublished - 2016
Event17th International Conference on Web Information Systems Engineering, WISE 2016 - Shanghai, China
Duration: Nov 8 2016Nov 10 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10041 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th International Conference on Web Information Systems Engineering, WISE 2016

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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