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CN 34-1054/N

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Distributed keyword approximate search method for RDF

  • College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.10.004
  • Received Date: 28 August 2016
  • Rev Recd Date: 08 December 2016
  • Publish Date: 31 October 2017
    Abstract

  • Abstract

    Existing RDF keyword search methods mainly search on the large-scale RDF data graph directly and do not make full use of the semantic information in the RDF ontology. Too many iterations lead to unfavorable search efficiency and unsatisfactory results. To solve these problems, a distributed keyword approximate search algorithm (DKASR) for RDF based on Redis memory database cluster was proposed and the parallel search of large-scale data on the distributed platform was realized. The algorithm constructs ontology sub-graphs by using the semantic information of RDF ontology, uses the semantic scoring function to sort ontology sub-graphs, and searches and returns the Top-k results concurrently with the aid of MapReduce computation model. If the results do not meet Top-k, ontology sub-graphs are extended to generate approximate ontology sub-graphs and the semantic similarity function is used to sort approximate ontology sub-graphs. Then, MapReduce computation model was used to realize the parallel search until the results meet Top-k. Finally, the results of experiments show that the DKASR algorithm can realize the RDF keyword approximate search and return the Top-k results efficiently and accurately.

    Abstract

    Existing RDF keyword search methods mainly search on the large-scale RDF data graph directly and do not make full use of the semantic information in the RDF ontology. Too many iterations lead to unfavorable search efficiency and unsatisfactory results. To solve these problems, a distributed keyword approximate search algorithm (DKASR) for RDF based on Redis memory database cluster was proposed and the parallel search of large-scale data on the distributed platform was realized. The algorithm constructs ontology sub-graphs by using the semantic information of RDF ontology, uses the semantic scoring function to sort ontology sub-graphs, and searches and returns the Top-k results concurrently with the aid of MapReduce computation model. If the results do not meet Top-k, ontology sub-graphs are extended to generate approximate ontology sub-graphs and the semantic similarity function is used to sort approximate ontology sub-graphs. Then, MapReduce computation model was used to realize the parallel search until the results meet Top-k. Finally, the results of experiments show that the DKASR algorithm can realize the RDF keyword approximate search and return the Top-k results efficiently and accurately.
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    • References(14)
  • [1]
    刘博. 基于关键词的RDF数据图查询模型研究[D]. 郑州：郑州大学, 2015.
    [2]
    姜旭,张波.采用RDF的查询扩展研究[J]. 计算机应用与软件, 2011, 28(12):210-212.
    JIANG Xu, ZHANG Bo. On query extension using RDF[J]. Computer Applications and Software, 2011, 28(12):210-212.
    [3]
    董书暕,汪璟玢. HMSST:一种高效的SPARQL查询优化算法[J]. 计算机科学, 2014, S2: 323-326, 336.
    DONG Shujian, WANG Jingbin. HMSST: An efficient algorithm for SPARQL query[J]. Computer Science, 2014, S2: 323-326, 336.
    [4]
    杜方,陈跃国,杜小勇. RDF数据查询处理技术综述[J]. 软件学报, 2013, 24(6): 1222-1242.
    DU Fang, CHEN Yueguo, DU Xiaoyong. Survey of RDF query processing techniques[J]. Journal of Software, 2013, 24(6): 1222-1242.
    [5]
    TRAN T, WANG H, RUDOLPH S, et al. Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data[C]// Proceedings of the 25th International Conference on Data Engineering. Shanghai, China: IEEE, 2009: 405-416.
    [6]
    ZENZ G, ZHOU X, MINACK E, et al. From keywords to semantic queries-Incremental query construction on the semantic Web[J]. Journal of Web Semantics, 2009, 7(3): 166-176.
    [7]
    GKIRTZOU K, PAPASTEFANATOS G, DALAMAGAS T. RDF keyword search based on keywords-to-sparql translation[C]// Proceedings of the First International Workshop on Novel Web Search Interfaces and Systems. Melbourne, Australia: ACM, 2015: 3-5.
    [8]
    LE W C, LI F F, KEMENTSIETSIDIS A, et al. Scalable keyword search on large RDF data[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(11): 2774-2788.
    [9]
    李慧颖,瞿裕忠. KREAG:基于实体三元组关联图的RDF数据关键词查询方法[J]. 计算机学报, 2011, 34(5): 825-835.
    LI Huiying, QU Yuzhong. KREAG：Keyword query approach over RDF data based on entity-triple association graph[J]. Chinese Journal of Computers, 2011, 34(5): 825-835.
    [10]
    ELBASSUONI S. Effective searching of RDF knowledge bases[D]. Munchen: Max-Planck-Institut Für Informatik, 2011.
    [11]
    KAOUDI Z, MANOLESCU I. RDF in the clouds: A survey[J]. VLDB Journal, 2015, 24(1): 67-91.
    [12]
    DE VIRGILIO R, MACCIONI A. Distributed Keyword Search Over RDF Via Mapreduce[M]// The Semantic Web: Trends and Challenges. Springer, 2014: 208-223.
    [13]
    周文健. 基于语义距离的RDF本体查询方法研究[D].沈阳: 东北大学, 2011.
    [14]
    章登义,吴文李,欧阳黜霏. 基于语义度量的RDF图近似查询[J].电子学报, 2015, 43(7): 1320-1328.
    ZHANG Dengyi, WU Wenli, OUYANG Chufei. Approximating query with semantic-based measure on RDF graphs[J]. Acta Electronica Sinica, 2015, 43(7): 1320-1328.
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    [1]
    刘博. 基于关键词的RDF数据图查询模型研究[D]. 郑州：郑州大学, 2015.
    [2]
    姜旭,张波.采用RDF的查询扩展研究[J]. 计算机应用与软件, 2011, 28(12):210-212.
    JIANG Xu, ZHANG Bo. On query extension using RDF[J]. Computer Applications and Software, 2011, 28(12):210-212.
    [3]
    董书暕,汪璟玢. HMSST:一种高效的SPARQL查询优化算法[J]. 计算机科学, 2014, S2: 323-326, 336.
    DONG Shujian, WANG Jingbin. HMSST: An efficient algorithm for SPARQL query[J]. Computer Science, 2014, S2: 323-326, 336.
    [4]
    杜方,陈跃国,杜小勇. RDF数据查询处理技术综述[J]. 软件学报, 2013, 24(6): 1222-1242.
    DU Fang, CHEN Yueguo, DU Xiaoyong. Survey of RDF query processing techniques[J]. Journal of Software, 2013, 24(6): 1222-1242.
    [5]
    TRAN T, WANG H, RUDOLPH S, et al. Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data[C]// Proceedings of the 25th International Conference on Data Engineering. Shanghai, China: IEEE, 2009: 405-416.
    [6]
    ZENZ G, ZHOU X, MINACK E, et al. From keywords to semantic queries-Incremental query construction on the semantic Web[J]. Journal of Web Semantics, 2009, 7(3): 166-176.
    [7]
    GKIRTZOU K, PAPASTEFANATOS G, DALAMAGAS T. RDF keyword search based on keywords-to-sparql translation[C]// Proceedings of the First International Workshop on Novel Web Search Interfaces and Systems. Melbourne, Australia: ACM, 2015: 3-5.
    [8]
    LE W C, LI F F, KEMENTSIETSIDIS A, et al. Scalable keyword search on large RDF data[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(11): 2774-2788.
    [9]
    李慧颖,瞿裕忠. KREAG:基于实体三元组关联图的RDF数据关键词查询方法[J]. 计算机学报, 2011, 34(5): 825-835.
    LI Huiying, QU Yuzhong. KREAG：Keyword query approach over RDF data based on entity-triple association graph[J]. Chinese Journal of Computers, 2011, 34(5): 825-835.
    [10]
    ELBASSUONI S. Effective searching of RDF knowledge bases[D]. Munchen: Max-Planck-Institut Für Informatik, 2011.
    [11]
    KAOUDI Z, MANOLESCU I. RDF in the clouds: A survey[J]. VLDB Journal, 2015, 24(1): 67-91.
    [12]
    DE VIRGILIO R, MACCIONI A. Distributed Keyword Search Over RDF Via Mapreduce[M]// The Semantic Web: Trends and Challenges. Springer, 2014: 208-223.
    [13]
    周文健. 基于语义距离的RDF本体查询方法研究[D].沈阳: 东北大学, 2011.
    [14]
    章登义,吴文李,欧阳黜霏. 基于语义度量的RDF图近似查询[J].电子学报, 2015, 43(7): 1320-1328.
    ZHANG Dengyi, WU Wenli, OUYANG Chufei. Approximating query with semantic-based measure on RDF graphs[J]. Acta Electronica Sinica, 2015, 43(7): 1320-1328.
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