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research-article

RDF knowledge graph keyword type search using frequent patterns

Authors: Wei Yan, Yuhan DingAuthors Info & Claims

Journal of Intelligent & Fuzzy Systems, Volume 41, Issue 1

Pages 2239 - 2253

https://doi.org/10.3233/JIFS-210950

Published: 01 January 2021 Publication History

Abstract

With the rapid development of Semantic Web, the retrieval of RDF data has become a research hotspot. As the main method of data retrieval, keyword search has attracted much attention because of its simple operation. The existing RDF keyword search methods mainly search directly on RDF graph, which is no longer applicable to RDF knowledge graph. Firstly, we propose to transform RDF knowledge graph data into type graph to prune the search space. Then based on type graph, we extract frequent search patterns and establish a list from frequent search patterns to pattern instances. Finally, we propose a method of the Bloom coding, which can be used to quickly judge whether the information our need is in frequent search patterns. The experiments show that our approach outperforms the state-of-the-art methods on both accuracy and response time.

References

[1]

Peng P., Zou L. and Qin Z., Answering top-K query combined keywords and structural queries on RDF graphs, Information Systems 67 (2017), 19–35.

Digital Library

[2]

Wu B., Zhou Y., Yuan P., Liu L. and Jin H., Scalable SPARQL querying using path partitioning, Proceedings of the 31st IEEE International Conference on Data Engineering, (2015), pp. 795–806.

[3]

Kasneci G., Ramanath M., Sozio M., Suchanek F.M. and Weikum G., STAR: steiner-tree approximation in relationship graphs, Proceedings of the 25th International Conference on Data Engineering, (2009), pp. 868–879.

[4]

Ding B., Yu J.X., Wang S., Qin L., Zhang X. and Lin X., Finding top-k min-cost connected trees in databases, Proceedings of the 23rd International Conference on Data Engineering, (2007), pp. 836–845.

[5]

He H., Wang H., Yang J. and Yu P.S., BLINKS: ranked keyword searches on graphs, Proceedings of the ACM SIGMOD International Conference on Management of Data, (2007), pp. 305–316.

[6]

Lian X., Hoyos E.D., Chebotko A., Fu B. and Reilly C., K-nearest keyword search in RDF graphs, Journal of Web Semantics 22 (2013), 40–56.

Digital Library

[7]

Gkirtzou K., Papastefanatos G. and Dalamagas T., RDF keyword search based on keywords-to-SPARQL translation, Proceedings of the First International Workshop on Novel Web Search Interfaces and Systems, (2015), pp. 3–5.

[8]

Zenz G., Zhou X., Minack E., Siberski W. and Nejdl W., From keywords to semantic queries-incremental query construction on the semantic Web, Journal of Web Semantics 7(3) (2009), 166–176.

Digital Library

[9]

RDF model and syntax specification, http://www.w3.org/TR/1999/REC-rdf-syntax-19990222/, 1999.

[10]

Elbassuoni S., Effective searching ofRDFknowledge bases, Saarland University, 2012.

[11]

Ladwig G. and Tran T., Combining query translation with query answering for efficient keyword search, Proceedings of the Semantic Web: Research and Applications, 7th Extended Semantic Web Conference, 2010, pp. 288–303.

[12]

Le W., Li F., Kementsietsidis A. and Duan S., Scalable keyword search on large RDF data, IEEE Transactions on Knowledge and Data Engineering 26(11) (2014), 2774–2788.

[13]

Bhalotia G., Hulgeri A., Nakhe C., Chakrabarti S. and Sudarshan S., Keyword searching and browsing in databases using BANKS, Proceedings of the 18th International Conference on Data Engineering, 2002, pp. 431–440.

[14]

Tran T., Wang H., Rudolph S. and Cimiano P., Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data, Proceedings of the 25th International Conference on Data Engineering, (2009), pp. 405–416.

[15]

Peng P., Zou L., Chen L. and Zhao D., Adaptive distributed RDF graph fragmentation and allocation based on query workload, IEEE Transactions on Knowledge and Data Engineering 31(4) (2019), 670–685.

Digital Library

[16]

Mountantonakis M. and Tzitzikas Y., Content-based union and complement metrics for dataset search over RDF knowledge graphs, Journal of Data and Information Quality 12(2) (2020), 10:1–10:31.

[17]

Shan Y., Li M. and Chen Y., Constructing target-aware results for keyword search on knowledge graphs, Data and Knowledge Engineering 110 (2017), 1–23.

[18]

Virgilio R.D., Efficient and effective ranking in Top-k exploration for keyword search on RDF, Proceedings of the IEEE International Conference on Information Reuse and Integration, (2011), pp. 66–70.

[19]

Lin X., Ma Z.M. and Yan L., RDF keyword search using a type-based summary, Journal of Information Science and Engineering 34(2) (2018), 489–504.

[20]

Ladwig G. and Tran T., Combining query translation with query answering for efficient keyword search, Proceedings of the Semantic Web: Research and Applications, (2010), pp. 288–303.

[21]

Lehmann J., Isele R., Jakob M., Jentzsch A., Kontokostas D., Mendes P.N., Hellmann S., Morsey M., Kleef P.V., Auer S. and Bizer C., DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia, Semantic Web 6(2) (2015), 167–195.

[22]

Wang X., Wang S., Xin Y., Yang Y., Li J. and Wang X., Distributedpregel-based provenance-aware regular path query processing onRDF knowledge graphs, World Wide Web:Internet and Web Information Systems 23(3) (2020), 1465–1496.

Digital Library

[23]

Zheng W., Zou L., Peng W., Yan X., Song S. and Zhao D., Semantic SPARQL similarity search over RDF knowledge graphs, Proceedings of the VLDB Endowment 9(11) (2016), 840–851.

Digital Library

[24]

Cai Z., Kalamatianos G., Fakas G.J., Mamoulis N. and Papadias D., Diversified spatial keyword search on RDF data, The VLDB Journal 29(5) (2020), 1171–1189.

[25]

Wang Q., Peng P., Tong T., Tian Z. and Qin Z., Keyword search over federated RDF systems, Proceedings of the Database Systems for Advanced Applications - 25th International Conference, (2020), pp. 613–622.

[26]

Kadilierakis G., Fafalios P., Papadakos P. and Tzitzikas Y., Keyword search over RDF using document-centric information retrieval systems, Proceedings of the Semantic Web- 17th International Conference, (2020), pp. 121–137.

[27]

Menendez E.S., Casanova M.A., Leme L.A. and Boughanem M., Novel node importance measures to improve keyword search over RDF graphs, Proceedings of the Database and Expert Systems Applications - 30th International Conferenc, (2019), pp. 143–158.

[28]

Dosso D. and Silvello G., A scalable virtual document-based keyword search system for RDF datasets, Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, (2019), pp. 965–968.

[29]

Guo Y., Pan Z. and Heflin J., LUBM: A benchmark for OWL knowledge base systems, Journal of Web Semantics 3(2-3) (2005), 158–182.

Digital Library

[30]

Coffman J. and Weaver A.C., An empirical performance evaluation of relational keyword search techniques, IEEE Transactions on Knowledge and Data Engineering 26(1) (2014), 30–42.

Digital Library

Cited By

Zhang JShen B(2024)A lightweight hierarchical graph convolutional model for knowledge graph representation learningApplied Intelligence10.1007/s10489-024-05787-254:21(10695-10708)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10489-024-05787-2
Zhang JShen BZhang Y(2024)Learning hierarchy-aware complex knowledge graph embeddings for link predictionNeural Computing and Applications10.1007/s00521-024-09775-y36:21(13155-13169)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09775-y
Zhang JShen BWang TZhong Y(2023)Converting hyperparameter gamma in distance-based loss functions to normal parameter for knowledge graph completionApplied Intelligence10.1007/s10489-023-04790-353:20(23369-23382)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10489-023-04790-3

Index Terms

RDF knowledge graph keyword type search using frequent patterns
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

Index terms have been assigned to the content through auto-classification.

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cover image Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology Volume 41, Issue 1

2021

2441 pages

ISSN:1064-1246

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© 2021 – IOS Press. All rights reserved.

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IOS Press

Netherlands

Publication History

Published: 01 January 2021

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Cited By

Zhang JShen B(2024)A lightweight hierarchical graph convolutional model for knowledge graph representation learningApplied Intelligence10.1007/s10489-024-05787-254:21(10695-10708)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10489-024-05787-2
Zhang JShen BZhang Y(2024)Learning hierarchy-aware complex knowledge graph embeddings for link predictionNeural Computing and Applications10.1007/s00521-024-09775-y36:21(13155-13169)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09775-y
Zhang JShen BWang TZhong Y(2023)Converting hyperparameter gamma in distance-based loss functions to normal parameter for knowledge graph completionApplied Intelligence10.1007/s10489-023-04790-353:20(23369-23382)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10489-023-04790-3

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