Abstract
Large-scale linked data is becoming a challenge to many Semantic Web tasks. While clustering of graphs has been deeply researched in network science and machine learning, not many researches are carried on clustering in linked data. To identify meta-structures in large-scale linked data, the scalability of clustering should be considered. In this paper, we propose a scalable approach of centrality-based clustering, which works on a model of Object Graph derived from RDF graph. Centrality of objects is calculated as indicators for clustering. Both relational and linguistic closeness between objects are considered in clustering to produce coherent clusters.
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Notes
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SWCC: http://data.semanticweb.org/.
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JAME: http://dbtune.org/jamendo/.
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LMDB: http://linkedmdb.org/.
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References
Hartig, O., Bizer, C., Freytag, J.-C.: Executing SPARQL queries over the web of linked data. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 293–309. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04930-9_19
Paulheim, H.: Exploiting linked open data as background knowledge in data mining. In: Proceedings of International Workshop on Data Mining on Linked Data, with Linked Data Mining Challenge Collocated with ECMLPKDD 2013, pp. 1–10 (2013)
Qu, Y., Hu, W., Cheng, G.: Constructing virtual documents for ontology matching. In: Proceedings of 15th International Conference on World Wide Web (WWW 2006), pp. 23–31 (2006)
Newman, M.E.J.: A measure of betweenness centrality based on random walks. Soc. Netw. 27, 39–54 (2005)
Zhang, X., Cheng, G., Qu, Y.: Ontology summarization based on RDF sentence graph. In: Proceedings of 16th International Conference on World Wide Web – WWW 2007, p. 707 (2007)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1998)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46, 668–677 (1999)
Lempel, R., Moran, S.: Stochastic approach for link-structure analysis (SALSA) and the TKC effect. Comput. Netw. 33, 387–401 (2000)
Sheskin, D.J.: Handbook of parametric and nonparametric statistical procedures. Technometrics 46, 1193 (2004)
Schlicht, A., Stuckenschmidt, H.: Towards structural criteria for ontology modularization. In: CEUR Workshop Proceedings (2006)
Grimnes, G.A.A, Edwards, P., Preece, A.: Instance based clustering of semantic web resources. In: Proceedings of 5th European Semantic Web Conference on the Semantic Web: Research and Applications, pp. 303–317 (2008)
Yan, Y., Wang, C., Zhou, A., Qian, W., Ma, L., Pan, Y.: Efficient indices using graph partitioning in RDF triple stores. In: Proceedings - International Conference on Data Engineering, pp. 1263–1266 (2009)
Aluç, G., Özsu, M.T., Daudjee, K.: Clustering RDF databases using tunable-LSH. pp. 1–13, CoRR, abs/1504.02523 (2015)
Tabrizi, S.A., Shakery, A., Asadpour, M., Abbasi, M., Tavallaie, M.A.: Personalized PageRank clustering: a graph clustering algorithm based on random walks. Phys. A Stat. Mech. Appl. 392, 5772–5785 (2013)
Acknowledgement
The work was supported by the National High-Tech Research and Development (863) Program of China (No. 2015AA015406) and the Open Project of Jiangsu Key Laboratory of Data Engineering and Knowledge Service (No. DEKS2014KT002).
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Zhang, X., Lv, Y., Lin, E. (2016). Object Clustering in Linked Data Using Centrality. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_17
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DOI: https://doi.org/10.1007/978-981-10-3168-7_17
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