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Multi-Target Search on Semantic Associations in Linked Data

Author

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  • Xiang Zhang

    (School of Computer Science and Engineering, Southeast University, Nanjing, China & Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing University, Nanjing, China)

  • Erjing Lin

    (School of Computer Science and Engineering, Southeast University, Nanjing, China)

  • Yulian Lv

    (College of Software Engineering (Suzhou), Southeast University, Suzhou, China)

Abstract

In this article, the authors propose a novel search model: Multi-Target Search (MT search in brief). MT search is a keyword-based search model on Semantic Associations in Linked Data. Each search contains multiple sub-queries, in which each sub-query represents a certain user need for a certain object in a group relationship. They first formularize the problem of association search, and then introduce their approach to discover Semantic Associations in large-scale Linked Data. Next, they elaborate their novel search model, the notion of Virtual Document they use to extract linguistic features, and the details of search process. The authors then discuss the way search results are organized and summarized. Quantitative experiments are conducted on DBpedia to validate the effectiveness and efficiency of their approach.

Suggested Citation

  • Xiang Zhang & Erjing Lin & Yulian Lv, 2018. "Multi-Target Search on Semantic Associations in Linked Data," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 14(1), pages 71-97, January.
    • Handle: RePEc:igg:jswis0:v:14:y:2018:i:1:p:71-97
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    Cited by:

    1. Wenguang Qian & Hua Li & Haiping Mu, 2022. "Circular LBP Prior-Based Enhanced GAN for Image Style Transfer," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(2), pages 1-15, April.

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