Leveraging Context Information for Joint Entity and Relation Linking

  • Yao Zhao
  • Zhuoming XuEmail author
  • Wei Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11809)


As an important module in most knowledge base question answering (KBQA) systems, entity and relation linking maps proper nouns and relational phrases to corresponding semantic constructs (entities and relations, respectively) in a given KB. Because different entities/relations may have the same mentions, joint disambiguation has been proposed to identify the exact entity/relation from a list of candidates using context information. Existing joint disambiguation methods, like the method in EARL (Entity and Relation Linker), mainly focus on modeling the co-occurrence probabilities of different entities and relations in input questions, while paying little attention to other non-mention expressions (e.g., wh-words). In this paper, we propose the Extended Entity and Relation Linker (EEARL), which leverages full context information to improve linking accuracy. EEARL firstly extracts the context information for each mention and the attribute features for each entity/relation via character-level and word-level LSTMs and constructs context vectors and feature vectors, respectively, and then calculates the similarity between the two vectors to re-score all the candidates. Experimental results on two benchmark datasets (LC-QuAD and QALD) show that EEARL outperforms EARL and several baseline methods in terms of both entity linking and relation linking accuracy.


Entity linking Relation linking Joint entity and relation linking knowledge base question answering Context information 


  1. 1.
    Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on Freebase from question-answer pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, pp. 1533–1544. Association for Computational Linguistics (2013).
  2. 2.
    Kolitsas, N., Ganea, O.-E., Hofmann, T.: End-to-end neural entity linking. In: Proceedings of the 22nd Conference on Computational Natural Language Learning, CoNLL 2018, pp. 519–529. Association for Computational Linguistics (2018).
  3. 3.
    Dubey, M., Banerjee, D., Chaudhuri, D., Lehmann, J.: EARL: joint entity and relation linking for question answering over knowledge graphs. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 108–126. Springer, Cham (2018). Scholar
  4. 4.
    Pintea, C.-M., Pop, P.C., Chira, C.: The generalized traveling salesman problem solved with ant algorithms. Complex Adapt. Syst. Model. 5, 8 (2017). Scholar
  5. 5.
    Lukovnikov, D., Fischer, A., Lehmann, J., Auer, S.: Neural network-based question answering over knowledge graphs on word and character level. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, pp. 1211–1220. International World Wide Web Conferences Steering Committee (2017).
  6. 6.
    Trivedi, P., Maheshwari, G., Dubey, M., Lehmann, J.: LC-QuAD: a corpus for complex question answering over knowledge graphs. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 210–218. Springer, Cham (2017). Scholar
  7. 7.
    Usbeck, R., Ngomo, A.-C.N., Haarmann, B., Krithara, A., Röder, M., Napolitano, G.: 7th open challenge on question answering over linked data (QALD-7). In: Dragoni, M., Solanki, M., Blomqvist, E. (eds.) SemWebEval 2017. CCIS, vol. 769, pp. 59–69. Springer, Cham (2017). Scholar
  8. 8.
    Ratinov, L., Roth, D., Downey, D., Anderson, M.: Local and global algorithms for disambiguation to Wikipedia. In: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, pp. 1375–1384. Association for Computational Linguistics (2011).
  9. 9.
    Yang, Y., Chang, M.-W.: S-MART: novel tree-based structured learning algorithms applied to tweet entity linking. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, ACL 2015, vol. 1, pp. 504–513. The Association for Computer Linguistics (2015).
  10. 10.
    Usbeck, R., et al.: AGDISTIS - graph-based disambiguation of named entities using linked data. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 457–471. Springer, Cham (2014). Scholar
  11. 11.
    Mulang, I.O., Singh, K., Orlandi, F.: Matching natural language relations to knowledge graph properties for question answering. In: Proceedings of the 13th International Conference on Semantic Systems, SEMANTICS 2017, pp. 89–96. ACM (2017).
  12. 12.
    Singh, K., et al.: Capturing knowledge in semantically-typed relational patterns to enhance relation linking. In: Proceedings of the Knowledge Capture Conference, K-CAP 2017, Article No. 31, pp. 31:1–31:8. ACM (2017).
  13. 13.
    Miller, G.A., Fellbaum, C.: WordNet then and now. Lang. Res. Eval. 41(2), 209–214 (2007). Scholar
  14. 14.
    Xu, K., Zhang, S., Feng, Y., Zhao, D.: Answering natural language questions via phrasal semantic parsing. In: Zong, C., Nie, J.Y., Zhao, D., Feng, Y. (eds.) Natural Language Processing and Chinese Computing. CCIS, vol. 496, pp. 333–344. Springer, Heidelberg (2014). Scholar
  15. 15.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011). Scholar
  16. 16.
    Akdal, B., ÇabukKeskin, Z.G., Ekinci, E.E., Kardas, G.: Model-driven query generation for ElasticSearch. In: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018, pp. 853–862. IEEE (2018).
  17. 17.
    Pinter, Y., Guthrie, R., Eisenstein, J.: Mimicking word embeddings using subword RNNs. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, pp. 102–112. Association for Computational Linguistics (2017).
  18. 18.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794. Association for Computing Machinery (2017).
  19. 19.
    Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, pp. 1532–1543. Association for Computational Linguistics (2014).
  20. 20.
    Zou, L., Huang, R., Wang, H., Yu, J.X., He, W., Zhao, D.: Natural language question answering over RDF - a graph data driven approach. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2014, pp. 313–324. ACM (2014).
  21. 21.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings the 7th International Conference on Semantic Systems, I-SEMANTICS 2011, pp. 1–8. ACM (2011).
  22. 22.
    Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. Trans. Assoc. Comput. Linguist. 2, 231–244 (2014). Scholar
  23. 23.
    Ristoski, P., Paulheim, H.: RDF2Vec: RDF graph embeddings for data mining. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 498–514. Springer, Cham (2016). Scholar
  24. 24.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, Conference Track Proceedings.
  25. 25.
    Boiński, T., Szymański, J., Dudek, B., Zalewski, P., Dompke, S., Czarnecka, M.: DBpedia and YAGO based system for answering questions in natural language. In: Nguyen, N.T., Pimenidis, E., Khan, Z., Trawiński, B. (eds.) ICCCI 2018. LNCS (LNAI), vol. 11055, pp. 383–392. Springer, Cham (2018). Scholar
  26. 26.
    Unger, C., Ngomo, A.-C.N., Cabrio, E.: 6th open challenge on question answering over linked data (QALD-6). In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.) SemWebEval 2016. CCIS, vol. 641, pp. 171–177. Springer, Cham (2016). Scholar
  27. 27.
    Speck, R., Ngomo, A.-C. N.: Ensemble learning of named entity recognition algorithms using multilayer perceptron for the multilingual web of data. In: Proceedings of the Knowledge Capture Conference, K-CAP 2017, Article No. 26, pp. 26:1–26:4. ACM (2017).
  28. 28.
    Zafar, H., Napolitano, G., Lehmann, J.: Formal query generation for question answering over knowledge bases. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 714–728. Springer, Cham (2018). Scholar

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Authors and Affiliations

  1. 1.College of Computer and InformationHohai UniversityNanjingChina
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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