Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

Multilingual Knowledge Base Completion by Cross-lingual Semantic Relation Inference


DOI: http://dx.doi.org/10.15439/2019F267

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 249253 ()

Full text

Abstract. Highly structured knowledge bases such as lexical semantic networks contain various connectivity patterns that can be learned as node features using dedicated frameworks. However, semantic relations are often unequally distributed over such knowledge resources. Some of the language partitions may benefit from integrating structured resources which are more easily available for resource-rich languages. In the present paper, we propose a simple endogenous method for enhancing a multilingual knowledge base through the cross-lingual semantic relation inference. It can be run on multilingual resources prior to semantic representation learning. Multilingual knowledge bases may integrate preexisting structured resources available for resource-rich languages. We aim at performing cross-lingual inference on them to improve the low resource language by creating semantic relationships.


  1. Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States., pages 2787–2795, 2013.
  2. Matthias Bröcheler, Lilyana Mihalkova, and Lise Getoor. Probabilistic similarity logic. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI’10, pages 73–82, Arlington, Virginia, United States, 2010. AUAI Press.
  3. Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R. Hruschka Jr., and Tom M. Mitchell. Toward an architecture for never-ending language learning. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15, 2010, 2010.
  4. Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. Convolutional 2d knowledge graph embeddings. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 1811–1818, 2018.
  5. Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pages 601–610, New York, NY, USA, 2014. ACM.
  6. Oren Etzioni, Michael Cafarella, Doug Downey, Ana-Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, and Alexander Yates. Unsupervised named-entity extraction from the web: An experimental study. Artificial Intelligence, 165(1):91 – 134, 2005.
  7. Christiane Fellbaum. WordNet An Electronic Lexical Database. The MIT Press, Cambridge, MA ; London, 1998.
  8. J. Ferber. Les systèmes multi-agents: vers une intelligence collective. InterEditions, Paris, 1995.
  9. Alexander F. Gelbukh. Inferences for enrichment of collocation databases by means of semantic relations. Computación y Sistemas, 22(1), 2018.
  10. Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2016:855–864, 2016.
  11. Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. A three-way model for collective learning on multi-relational data. In Proceedings of the 28th International Conference on Machine Learning, ICML’11, pages 809–816, USA, 2011. Omnipress.
  12. Jerónimo Hernández-González, Estevam R. Hruschka Jr., and Tom M. Mitchell. Merging knowledge bases in different languages. In Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing, pages 21–29, Vancouver, Canada, August 2017. Association for Computational Linguistics.
  13. Mathieu Lafourcade. Lexique et analyse sémantique de textes - structures, acquisitions,calculs, et jeux de mots. (Lexicon and semantic analysis of texts - structures, acquisition, computation and games with words). Montpellier, 2011.
  14. Mathieu Lafourcade and Lionel Ramadier. Semantic RelationExtraction with Semantic Patterns: Experiment on Radiology Report. In LREC 2016 Conference on Language Resources and Evaluation, volume 10th of LREC 2016 Proceedings, Portorož, Slovenia, May 2016.
  15. Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. A review of relational machine learning for knowledge graphs: From multi-relational link prediction to automated knowledge graph construction. CoRR, abs/1503.00759, 2015.
  16. Lionel Ramadier. Indexation and learning of terms and relations from reports of radiology. Theses, Université de Montpellier, November 2016.
  17. Matthew Richardson and Pedro Domingos. Markov logic networks. Mach. Learn., 62(1-2):107–136, February 2006.
  18. Gilles Sérasset. Dbnary: Wiktionary as a lmf based multilingual rdf network. In LREC, 2012.
  19. Gilles Sérasset. DBnary: Wiktionary as a Lemon-Based Multilingual Lexical Resource in RDF. Semantic Web – Interoperability, Usability, Applicability, pages –, 2014. To appear.
  20. Jaeho Shin, Sen Wu, Feiran Wang, Christopher De Sa, Ce Zhang, and Christopher Ré. Incremental knowledge base construction using deepdive. Proc. VLDB Endow., 8(11):1310–1321, July 2015.
  21. Robert Speer and Catherine Havasi. Representing general relational knowledge in conceptnet 5. In LREC Proceedings, 2012.
  22. Fabian Suchanek, Gjergji M Kasneci, and Gerhard M Weikum. Yago: A Core of Semantic KnowledgeUnifying WordNet and Wikipedia. In 16th international conference on World Wide Web, Proceedings of the 16th international conference on World Wide Web, pages 697 – 697, Banff, Canada, May 2007.
  23. Kristina Toutanova and Danqi Chen. Observed versus latent features for knowledge base and text inference. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, pages 57–66, Beijing, China, July 2015. Association for Computational Linguistics.
  24. Quan Wang, Bin Wang, and Li Guo. Knowledge base completion using embeddings and rules. In Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI’15, pages 1859–1865. AAAI Press, 2015.
  25. Manel Zarrouk, Mathieu Lafourcade, and Alain Joubert. Inference and Reconciliation in a Crowdsourced Lexical-Semantic Network. In CICLING: International Conference on Intelligent Text Processing and Computational Linguistics, number 14th, Samos, Greece, March 2013.