Abstract
Knowledge graphs contain only a subset of what is true. Following recent success of Question Answering systems in outperforming humans, we employ the developed tools to complete knowledge graph. To create the questions automatically, we explore domain-specific lexicalization patterns. We outline the overall procedure and discuss preliminary results.
This work has been partially funded by the project LYNX. The project LYNX has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 780602. More information is available online at http://www.lynx-project.eu.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abedjan, Z., Naumann, F.: Improving RDF data through association rule mining. Datenbank-Spektrum 13(2), 111–120 (2013)
Bhogal, J., Macfarlane, A., Smith, P.: A review of ontology based query expansion. Inf. Process. Manag. 43(4), 866–886 (2007)
d’Amato, C., Staab, S., Tettamanzi, A.G.B., Minh, T.D., Gandon, F.: Ontology enrichment by discovering multi-relational association rules from ontological knowledge bases. In: ACM 31, pp. 333–338 (2016)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018)
Ell, B., Harth, A.: A language-independent method for the extraction of RDF verbalization templates. In: INLG 2014, pp. 26–34 (2014)
Ho, V.T., Stepanova, D., Gad-Elrab, M.H., Kharlamov, E., Weikum, G.: Rule learning from knowledge graphs guided by embedding models. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 72–90. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00671-6_5
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: ACL 2015, Long Papers,, vol. 1, pp. 687–696 (2015)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI 29, pp. 2181–2187 (2015)
Sanchez-Cisneros, D., Aparicio Gali, F.: UEM-UC3M: an ontology-based named entity recognition system for biomedical texts. In: SemEval, pp. 622–627. Association for Computational Linguistics (2013)
Schutz, A., Buitelaar, P.: RelExt: a tool for relation extraction from text in ontology extension. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 593–606. Springer, Heidelberg (2005). https://doi.org/10.1007/11574620_43
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI 2014, pp. 1112–1119 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Khvalchik, M., Blaschke, C., Revenko, A. (2019). Question Formulation and Question Answering for Knowledge Graph Completion. In: Anderst-Kotsis, G., et al. Database and Expert Systems Applications. DEXA 2019. Communications in Computer and Information Science, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-27684-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-27684-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27683-6
Online ISBN: 978-3-030-27684-3
eBook Packages: Computer ScienceComputer Science (R0)