Skip to main content

Question Formulation and Question Answering for Knowledge Graph Completion

  • Conference paper
  • First Online:
Database and Expert Systems Applications (DEXA 2019)

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.w3.org/TR/r2rml/.

  2. 2.

    https://rajpurkar.github.io/SQuAD-explorer/.

  3. 3.

    https://wiki.dbpedia.org/.

  4. 4.

    https://www.ncbi.nlm.nih.gov/pubmed/.

  5. 5.

    https://www.drugbank.ca/drugs/DB12332.

  6. 6.

    https://www.ncbi.nlm.nih.gov/pubmed/27002934.

  7. 7.

    https://www.fda.gov.

References

  1. Abedjan, Z., Naumann, F.: Improving RDF data through association rule mining. Datenbank-Spektrum 13(2), 111–120 (2013)

    Article  Google Scholar 

  2. Bhogal, J., Macfarlane, A., Smith, P.: A review of ontology based query expansion. Inf. Process. Manag. 43(4), 866–886 (2007)

    Article  Google Scholar 

  3. 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)

    Google Scholar 

  4. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018)

    Google Scholar 

  5. Ell, B., Harth, A.: A language-independent method for the extraction of RDF verbalization templates. In: INLG 2014, pp. 26–34 (2014)

    Google Scholar 

  6. 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

    Chapter  Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. 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

    Chapter  Google Scholar 

  11. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI 2014, pp. 1112–1119 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maria Khvalchik .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics