Abstract
Semantic association represents group relationship among objects in linked data. Searching semantic associations is complicated, which involves the search of multiple objects and the search of their group relationships simultaneously. In this paper, we propose this kind of search as a multi-target search, and we compare it to traditional search tasks, which we classify as single-target search. A novel search model is introduced, and the notion of virtual document is used to extract linguistic information of semantic associations. Multi-target search is finally fulfilled by a PageRank-like ranking scheme and a top-K selection policy considering object affinity. Experiments show that our approach is effective in improving retrieval precision on semantic associations.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhang, X., Zhao, C., Wang, P., Zhou, F.: Mining link patterns in linked data. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds.) WAIM 2012. LNCS, vol. 7418, pp. 83–94. Springer, Heidelberg (2012)
Wang, C., Zhang, X., Lv, Y., Ji, L., Wang, P.: Searching semantic associations based on virtual document. In: Qi, G., Tang, J., Du, J., Pan, J.Z., Yu, Y. (eds.) CSWS 2013. CCIS, vol. 406, pp. 62–75. Springer, Heidelberg (2013)
Aleman-Meza, B., Halaschek-Wiener, C., Arpinar, I.B., Sheth, A.P.: Context-aware semantic association ranking, vol. 1, no. 3, pp. 33–50 (2003)
Kochut, K.J., Janik, M.: SPARQLeR: extended SPARQL for semantic association discovery. Semant. Web Res. Appl. 4519, 145–159 (2007)
Le, B.T., Dieng-Kuntz, R., Gandon, F.: On ontology matching problems. In: Proceedings of the International Conference on Enterprize Information Systems, pp. 236–243 (2003)
Lee, M., Kim, W.: Semantic association search and rank method based on spreading activation for the semantic web. In: Proceedings of the International Conference on Industrial Engineering and Engineering Management, pp. 523–1527 (2009)
Viswanathan, V., Krishnamurthi, I.: Finding relevant semantic association paths through user-specific intermediate entities. Hum. Centric Comput. Inf. Sci. 2(1), 1–11 (2012)
Qu, Y., Hu, W., Cheng, G.: Constructing virtual documents for ontology matching. In: Proceedings of the International Conference on World Wide Web, pp. 23–31 (2006)
Tran, T., Wang, H., Rudolph, S., Cimiano, P.: Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data. In Proceedings of the IEEE International Conference on Data Engineering, pp. 405–416 (2009)
Li, H., Wang, Y.: Ranked keyword query on semantic web data. In: Proceedings of the International Conference on Fuzzy Systems and Knowledge Discovery, pp. 2285–2289 (2010)
Acknowledgements
The work was supported by the National High-Tech Research and Development (863) Program of China (No. 2015AA015406), the Open Project of Jiangsu Key Laboratory of Data Engineering and Knowledge Service (No. DEKS2014KT002), and National Natural Science Foundation of China (No. 61472077). We would like to thank Xing Li for his efforts in implementation and evaluations.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Zhang, X., Lv, Y. (2016). Towards Multi-target Search of Semantic Association. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-50112-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50111-6
Online ISBN: 978-3-319-50112-3
eBook Packages: Computer ScienceComputer Science (R0)