DocumentCode :
73350
Title :
Scalable Keyword Search on Large RDF Data
Author :
Wangchao Le ; Feifei Li ; Kementsietsidis, Anastasios ; Songyun Duan
Author_Institution :
Sch. of Comput., Univ. of Utah, Salt Lake City, UT, USA
Volume :
26
Issue :
11
fYear :
2014
fDate :
Nov. 2014
Firstpage :
2774
Lastpage :
2788
Abstract :
Keyword search is a useful tool for exploring large RDF data sets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building summaries from the RDF graphs for query processing. In this work, we show that existing techniques have serious limitations in dealing with realistic, large RDF data with tens of millions of triples. Furthermore, the existing summarization techniques may lead to incorrect/incomplete results. To address these issues, we propose an effective summarization algorithm to summarize the RDF data. Given a keyword query, the summaries lend significant pruning powers to exploratory keyword search and result in much better efficiency compared to previous works. Unlike existing techniques, our search algorithms always return correct results. Besides, the summaries we built can be updated incrementally and efficiently. Experiments on both benchmark and large real RDF data sets show that our techniques are scalable and efficient.
Keywords :
graph theory; meta data; query processing; semantic Web; distance matrix; effective summarization algorithm; exploratory keyword search; large RDF data; query processing; scalable keyword search; Buildings; Joining processes; Keyword search; Query processing; Resource description framework; Rockets; Standards; Keywords search; RDF data; RDF graph;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2014.2302294
Filename :
6720109
Link To Document :
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