TY - GEN
T1 - Diversifying the results of keyword queries on linked data
AU - Dass, Ananya
AU - Aksoy, Cem
AU - Dimitriou, Aggeliki
AU - Theodoratos, Dimitri
AU - Wu, Xiaoying
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84996554601&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-48740-3_14
DO - 10.1007/978-3-319-48740-3_14
M3 - Conference contribution
AN - SCOPUS:84996554601
SN - 9783319487397
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 199
EP - 207
BT - Web Information Systems Engineering – WISE 2016 - 17th International Conference, Proceedings
A2 - Cellary, Wojciech
A2 - Wang, Jianmin
A2 - Mokbel, Mohamed F.
A2 - Wang, Hua
A2 - Zhou, Rui
A2 - Zhang, Yanchun
PB - Springer Verlag
T2 - 17th International Conference on Web Information Systems Engineering, WISE 2016
Y2 - 8 November 2016 through 10 November 2016
ER -