Abstract
Linked Data is an utterly valuable component for semantic technologies because it can be used for accessing and distributing knowledge from one data source to other data sources via structured links. Therefore, mapping instances to Linked Data resources plays a key role for consuming knowledge in Linked Data resources so that we can understand instances more precisely. Since an instance, which can be aligned to Linked Data resources, is enriched its information by other instances, the instance then is full of information, which perfectly describes itself. Nevertheless, mapping instances to Linked Data resources is still challenged due to the heterogeneity problem and the multiple data source problem as well. Most techniques focus on mapping instances between two specific data sources and deal with the heterogeneity problem. Mapping instances particularly relying on two specific data sources is not enough because it will miss an opportunity to map instances to other sources. We therefore present the Instance Expansion Framework, which automatically discover and map instances more than two specific data sources in Linked Data resources. The framework consists of three components: Candidate Selector, Instance Matching and Candidate Expander. Experiments show that the Candidate Expander component is significantly important for mapping instances to Linked Data resources.
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References
Berners-Lee, T.: Linked data - design issues (2006). http://www.w3.org/DesignIssues/LinkedData.html
Klyne, G., Carroll, J.J.: Resource Description Framework (RDF): Concepts and abstract syntax, W3C Recommendation (2004). http://www.w3.org/TR/rdf-concepts/
Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. J. Semant. Web Inf. Syst. 4(2), 1–22 (2009)
Bechhofer, S., Harmelen, F., Hendler, J., Horrocks, I., McGuinness, D.L., Patel-Schneider, P.F., Andrea Stein, L.: OWL web ontology language reference, W3C Recommendation (2004). http://www.w3.org/TR/owl-ref/
Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Discovering and maintaining links on the web of data. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 650–665. Springer, Heidelberg (2009)
Euzenat, J., Ferrara, W., Hage, A., Hollink, L., Meilicke, C., Nikolov, A., Scharffe, F., Shvaiko, P., Stuckenschmidt, H., Zamazal, O., Trojahn, C.: Final results of the ontology alignment evaluation initiative 2011. In: Proceedings of the 6th Workshop on Ontology Matching, pp. 85–113 (2011)
Niu, X., Rong, S., Zhang, Y., Wang, H.: Zhishi.links results for OAEI 2011. In: Proceedings of the 6th Workshop on Ontology Matching, pp. 220–227 (2011)
Hu, W., Chen, J., Qu, Y.: A self-training approach for resolving object coreference on the semantic web. In: Proceedings of the 20th International Conference on World Wide Web, pp. 87–96. ACM (2011)
Araujo, S., Tran, D., de Vries, A., Hidders, J., Schwabe, D.: SERIMI: Class-based disambiguation for effective instance matching over heterogeneous web data. In: The 15th Workshop on Web and Database Proc., pp. 19–25 (2012)
Nguyen, K., Ichise, R., Le, B.: Learning approach for domain-independent linked data instance matching. In: Proceedings of the 2nd Workshop on Mining Data Semantics, no. 7 (2012)
Nguyen, K., Ichise, R., Le, B.: SLINT: a schema-independent linked data interlinking system. In: Proceedings of the 7th Workshop on Ontology Matching, pp. 1–12 (2012)
Nguyen, K., Ichise, R., Le, B.: Interlinking linked data sources using a domain-independent system. In: Proceedings of the 2nd Joint International Semantic Technology Conference, pp. 113–128 (2012)
Rong, S., Niu, X., Xiang, E.W., Wang, H., Yang, Q., Yu, Y.: A machine learning approach for instance matching based on similarity metrics. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 460–475. Springer, Heidelberg (2012)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)
Cohen, W., Ravikumar, P., Fienberg, S.: A comparison of string metrics for matching names and records. In: Proceedings of the Workshop on Data Cleaning and Object Consolidation (2003)
Li, J., Tang, J., Li, Y., Luo, Q.: RiMON: a dynamic multistrategy ontology alignment framework. IEEE Trans. Knowl. Data Eng. 21(8), 1218–1232 (2009)
Caimi, F.: Ontology and instance matching for the linked open data cloud. Master Thesis of University of Illinois at Chicago (2012)
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Kertkeidkachorn, N., Ichise, R., Suchato, A., Punyabukkana, P. (2014). An Automatic Instance Expansion Framework for Mapping Instances to Linked Data Resources. In: Kim, W., Ding, Y., Kim, HG. (eds) Semantic Technology. JIST 2013. Lecture Notes in Computer Science(), vol 8388. Springer, Cham. https://doi.org/10.1007/978-3-319-06826-8_28
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