Skip to main content
SpringerLink
Log in

Leveraging Context Information for Joint Entity and Relation Linking

  • Conference paper
  • First Online:
Web and Big Data (APWeb-WAIM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11809))

  • 691 Accesses

Abstract

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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). https://www.aclweb.org/anthology/D13-1160

  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). https://aclweb.org/anthology/papers/K/K18/K18-1050/

  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). https://doi.org/10.1007/978-3-030-00671-6_7

    Chapter  Google Scholar 

  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). https://doi.org/10.1186/s40294-017-0048-9

    Article  Google Scholar 

  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). https://doi.org/10.1145/3038912.3052675

  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). https://doi.org/10.1007/978-3-319-68204-4_22

    Chapter  Google Scholar 

  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). https://doi.org/10.1007/978-3-319-69146-6_6

    Chapter  Google Scholar 

  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). https://www.aclweb.org/anthology/P11-1138

  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). https://www.aclweb.org/anthology/P15-1049

  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). https://doi.org/10.1007/978-3-319-11964-9_29

    Chapter  Google Scholar 

  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). https://doi.org/10.1145/3132218.3132229

  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). https://doi.org/10.1145/3148011.3148031

  13. Miller, G.A., Fellbaum, C.: WordNet then and now. Lang. Res. Eval. 41(2), 209–214 (2007). https://doi.org/10.1007/s10579-007-9044-6

    Article  Google Scholar 

  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). https://doi.org/10.1007/978-3-662-45924-9_30

    Chapter  Google Scholar 

  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). https://dl.acm.org/citation.cfm?id=2078186

    MATH  Google Scholar 

  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). https://doi.org/10.15439/2018F218

  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). https://www.aclweb.org/anthology/D17-1010

  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). https://doi.org/10.1145/2939672.2939785

  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). https://www.aclweb.org/anthology/D14-1162

  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). https://doi.org/10.1145/2588555.2610525

  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). https://doi.org/10.1145/2063518.2063519

  22. Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. Trans. Assoc. Comput. Linguist. 2, 231–244 (2014). https://transacl.org/ojs/index.php/tacl/article/view/291

    Article  Google Scholar 

  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). https://doi.org/10.1007/978-3-319-46523-4_30

    Chapter  Google Scholar 

  24. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, Conference Track Proceedings. https://arxiv.org/abs/1412.6980

  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). https://doi.org/10.1007/978-3-319-98443-8_35

    Chapter  Google Scholar 

  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). https://doi.org/10.1007/978-3-319-46565-4_13

    Chapter  Google Scholar 

  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). https://doi.org/10.1145/3148011.3154471

  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). https://doi.org/10.1007/978-3-319-93417-4_46

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing, 210098, China

    Yao Zhao & Zhuoming Xu

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Wei Hu

Authors
  1. Yao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhuoming Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhuoming Xu .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Jingkuan Song

  2. Massey University, Auckland, New Zealand

    Xiaofeng Zhu

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

Zhao, Y., Xu, Z., Hu, W. (2019). Leveraging Context Information for Joint Entity and Relation Linking. In: Song, J., Zhu, X. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11809. Springer, Cham. https://doi.org/10.1007/978-3-030-33982-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33982-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33981-4

  • Online ISBN: 978-3-030-33982-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation