Abstract
In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, and (iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships in the field of Semantic Web.
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References
Angeli, G., Premkumar, M.J.J., Manning, C.D.: Leveraging linguistic structure for open domain information extraction. In: Proceedings of the 53rd Annual Meeting of the ACL and the 7th IJCNLP, vol. 1, pp. 344–354 (2015)
Auer, S., Kovtun, V., Prinz, M., Kasprzik, A., Stocker, M., Vidal, M.E.: Towards a knowledge graph for science. In: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, p. 1. ACM (2018)
Dessì, D., Reforgiato Recupero, D., Fenu, G., Consoli, S.: A recommender system of medical reports leveraging cognitive computing and frame semantics. In: Tsihrintzis, G.A., Sotiropoulos, D.N., Jain, L.C. (eds.) Machine Learning Paradigms. ISRL, vol. 149, pp. 7–30. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94030-4_2
Ehrlinger, L., Wöß, W.: Towards a definition of knowledge graphs. In: SEMANTiCS (Posters, Demos, SuCCESS), vol. 48 (2016)
Luan, Y., He, L., Ostendorf, M., Hajishirzi, H.: Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. In: Proceedings of the EMNLP 2018 Conference, pp. 3219–3232 (2018)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Nuzzolese, A.G., Gentile, A.L., Presutti, V., Gangemi, A.: Conference linked data: the ScholarlyData project. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 150–158. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46547-0_16
Peroni, S., Shotton, D., Vitali, F.: One year of the OpenCitations corpus. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 184–192. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68204-4_19
Salatino, A.A., Thanapalasingam, T., Mannocci, A., Osborne, F., Motta, E.: Classifying research papers with the computer science ontology. In: ISWC (P&D/Industry/BlueSky). CEUR Workshop Proceedings, vol. 2180 (2018)
Salatino, A.A., Thanapalasingam, T., Mannocci, A., Osborne, F., Motta, E.: The computer science ontology: a large-scale taxonomy of research areas. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11137, pp. 187–205. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00668-6_12
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Buscaldi, D., Dessì, D., Motta, E., Osborne, F., Reforgiato Recupero, D. (2019). Mining Scholarly Publications for Scientific Knowledge Graph Construction. In: Hitzler, P., et al. The Semantic Web: ESWC 2019 Satellite Events. ESWC 2019. Lecture Notes in Computer Science(), vol 11762. Springer, Cham. https://doi.org/10.1007/978-3-030-32327-1_2
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DOI: https://doi.org/10.1007/978-3-030-32327-1_2
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