Abstract
In this paper, we propose a method to reduce the difficulties of query caused by lack of information about graph patterns even though the graph pattern is one of the important characteristics of the LOD. To do so, we apply the clustering methodology to find the RDF predicates that have similar patterns. In addition, we identify representative graph patterns that imply its characteristics each cluster. The representative graph patterns are used to extend the users’ query graphs. To show the difficulties of the query on the LOD, we developed an illustrative example. We propose the novel framework to support query extension using predicate clustering-based entity-centered graph patterns. Through the implementation of this framework, the user can easily query the LOD and at the same time collect appropriate query results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lausch, A., Schmidt, A., Tischendorf, L.: Data mining and linked open data – new perspectives for data analysis in environmental research. Ecol. Model. 295, 5–17 (2015)
Ristoski, P., Paulheim, H.: Semantic web in data mining and knowledge discovery: a comprehensive survey. Web Semant. Sci. Serv. Agents World Wide Web 36, 1–22 (2016)
Ristoski, P., Paulheim, H.: RDF2Vec: RDF graph embeddings for data mining. In: The Semantic Web – ISWC 2016, pp. 498–514 (2016)
Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining, vol. 400, no. 1, pp. 525–526, August 2000
Hierarchical Clustering, Clustering, pp. 31–62
Harth, A., Speiser, S.: On completeness classes for query evaluation on linked data. In: AAAI, July 2012
Karagiannis, D., Buchmann, R.A.: Linked open models: extending linked open data with conceptual model information. Inf. Syst. 56, 174–197 (2016)
Makris, K., Bikakis, N., Gioldasis, N., Christodoulakis, S.: SPARQL-RW. In: Proceedings of the 15th International Conference on Extending Database Technology - EDBT 2012 (2012)
Yih, W., Chang, M.-W., He, X., Gao, J.: Semantic parsing via staged query graph generation: question answering with knowledge base. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2015)
Zheng, W., Zou, L., Peng, W., Yan, X., Song, S., Zhao, D.: Semantic SPARQL similarity search over RDF knowledge graphs. Proc. VLDB Endowment 9(11), 840–851 (2016)
Li, J., Wang, W.: Graph summarization for source selection of querying over Linked Open Data. In: 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), December 2017
Zou, L., Özsu, M.T., Chen, L., Shen, X., Huang, R., Zhao, D.: gStore: a graph-based SPARQL query engine. VLDB J. 23(4), 565–590 (2013)
Vander Sande, M., Verborgh, R., Dimou, A., Colpaert, P., Mannens, E.: Hypermedia-based discovery for source selection using low-cost linked data interfaces. Int. J. Semant. Web Inf. Syst. 12(3), 79–110 (2016)
Vandenbussche, P.-Y., Atemezing, G.A., Poveda-Villalón, M., Vatant, B.: Linked Open Vocabularies (LOV): a gateway to reusable semantic vocabularies on the web. Semant. Web 8(3), 437–452 (2016)
Acknowledgements
This research is supported by C2 integrating and interfacing technologies laboratory of Agency for Defense Development (UD180014ED).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Kim, J., Kong, J., Park, D., Sohn, M. (2019). Predicate Clustering-Based Entity-Centered Graph Pattern Recognition for Query Extension on the LOD. In: Barolli, L., Xhafa, F., Javaid, N., Enokido, T. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2018. Advances in Intelligent Systems and Computing, vol 773. Springer, Cham. https://doi.org/10.1007/978-3-319-93554-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-93554-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93553-9
Online ISBN: 978-3-319-93554-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)