Abstract
The development of digital library increases the need of integrating, enriching and republishing library data as Linked Data. Linked library data could provide high quality and more tailored service for library management agencies as well as for the public. However, even though there are many data sets containing metadata about publications and researchers, it is cumbersome to integrate and analyze them, since the collection is still a manual process and the sources are not connected to each other upfront. In this paper, we present an approach for integrating, enriching and republishing library data as Linked Data. In particular, we first adopt duplication detection and disambiguation techniques to reconcile researcher data, and then we connect researcher data with publication data such as papers, patents and monograph using entity linking methods. After that, we use simple reasoning to predict missing values and enrich the library data with external data. Finally, we republish the integrated and enriched library data as Linked Data.
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References
Igata, N., Nishino, F., Kume, T., Matsutsuka, T.: Information integration and utilization technology using linked data. FUJITSU Sci. Tech. J. 50(1), 3–8 (2014)
Krafft, D.B.: Linked data for libraries: a project update. In: 14th International Semantic Web Conference, United States of America, Bethlehem, pp. 11–15 (2015)
Monz, C., Weerkamp, W.: A comparison of retrieval-based hierarchical clustering approaches to person name disambiguation. In: 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 65–651 (2009)
Yoshida, M., Ikeda, M., Ono, S., Sato, I., Nakagawa, H.: Person name disambiguation by bootstrapping. In: 33th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 10–17 (2010)
Xu, J., Lu, Q., Liu, Z.: Combining classification with clustering for web person disambiguation. In: 21st International Conference on World Wide Web, pp. 637–638 (2012)
Cucerzan, S.: Large-scale named entity disambiguation based on wikipedia data. In: 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 708–716 (2007)
Damljanovic, D., Bontcheva, K.: Named entity disambiguation using linked data, In: 9th Extended Semantic Web Conference (2012)
Usbeck, R., Ngonga Ngomo, A.-C., Röder, M., Gerber, D., Coelho, S.A., Auer, S., Both, A.: AGDISTIS - graph-based disambiguation of named entities using linked data. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 457–471. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11964-9_29
Mihalcea, R., Csomai, A.: Wikify! linking documents to encyclopedic knowledge. In: 17th ACM Conference on Information and Knowledge Management, pp. 233–242 (2007)
Milne, D., Witten, I.H.: Learning to link with wikipedia. In: 17th ACM Conference on Information and Knowledge Management, pp. 509–518 (2008)
Han, X.P., Sun, L.: A generative entity-mention model for linking entities with knowledge base. In: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 945–954 (2011)
Ferragina, P., Scaiella, U.: TAGME: on-the-fly annotation of short text fragments. In: 19th ACM International Conference on Information and Knowledge Management, pp. 1625–1628 (2010)
Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: 7th International Conference on Semantic Systems, pp. 1–8 (2011)
Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: 13th Conference on Computational Natural Language Learning, pp. 147–155 (2009)
Yosef, M.A., Hoffart, J., Bordino, I., Spaniol, M., Weikum, G.: AIDA: an online tool for accurate disambiguation of named entities in text and tables. In: PVLDB 2011, pp. 1450–1453 (2011)
Hu, W., Qu, Y., Cheng, G.: Matching large ontologies: a divide-and-conquer approach. Data Knowl. Eng. 67(1), 140–160 (2008)
Jimenez-Ruiz, E., Grau, B.C., Zhou, Y.: Logmap 2.0: towards logic-based, scalable and interactive ontology matching. In: Ontology Matching, pp. 45–46 (2011)
Li, Y., Li, J.Z., Zhang, D., Tang, J.: Result of ontology alignment with RiMOM at OAEI 2006. In: Ontology Matching (2006)
Suchanek, F.M., Abiteboul, S., Senellart, P.: PARIS: probabilistic alignment of relations, instances, and schema. PVLDB 5(3), 157–168 (2011)
Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - a crystallization point for the web of data. J. Web Semant. 7, 154–165 (2009)
Niu, X., Sun, X., Wang, H., Rong, S., Qi, G., Yu, Y.: Zhishi.me - weaving chinese linking open data. In: Proceedings of 10th International Semantic Web Conference, Bonn, pp. 23–27 (2011)
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Miao, Q. et al. (2016). LD2LD: Integrating, Enriching and Republishing Library Data as Linked Data. 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_16
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DOI: https://doi.org/10.1007/978-981-10-3168-7_16
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