Abstract
Question answering over Knowledge Graphs has emerged as an intuitive way of querying structured data sources and has witnessed significant progress over the years. However, there is still plenty of space for improvement and there exist specific challenges that are still far from being effectively solved. In this research project, we aim to address some of these challenges and provide innovative solutions in the field. Our research will mainly focus on deep learning approaches such as sequence to sequence models and ranking methods. We plan to contribute to the challenges of explicability and complex queries by further researching the areas and providing resources together with more robust models by using probabilistic methods and meta-learning approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Agarwal, R., Liang, C., Schuurmans, D., Norouzi, M.: Learning to generalize from sparse and underspecified rewards. arXiv e-prints (2019)
Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar. Association for Computational Linguistics (2014)
Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks. arXiv e-prints (2015)
Chakraborty, N., Lukovnikov, D., Maheshwari, G., Trivedi, P., Lehmann, J., Fischer, A.: Introduction to neural network based approaches for question answering over knowledge graphs. arXiv e-prints (2019)
Cheng, J., Lapata, M.: Weakly-supervised neural semantic parsing with a generative ranker. arXiv e-prints (2018)
Diefenbach, D., Dridi, Y., Singh, K., Maret, P.: SPARQLtoUser: did the question answering system understand me? In: ISWC 2017 (2017)
Dong, L., Lapata, M.: Coarse-to-fine decoding for neural semantic parsing. arXiv e-prints (2018)
Dong, L., Wei, F., Zhou, M., Xu, K.: Question answering over Freebase with multi-column convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China. Association for Computational Linguistics (2015)
Dubey, M., Banerjee, D., Abdelkawi, A., Lehmann, J.: LC-QuAD 2.0: a large dataset for complex question answering over Wikidata and DBpedia. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11779, pp. 69–78. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30796-7_5
Ell, B., Harth, A., Simperl, E.: SPARQL query verbalization for explaining semantic search engine queries. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 426–441. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_29
Ferré, S.: SPARKLIS: an expressive query builder for SPARQL endpoints with guidance in natural language. Semant. Web 8, 405–418 (2017)
Guo, D., Tang, D., Duan, N., Zhou, M., Yin, J.: Coupling retrieval and meta-learning for context-dependent semantic parsing. arXiv e-prints (2019)
He, X., Golub, D.: Character-level question answering with attention. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas. Association for Computational Linguistics (2016)
Kacupaj, E., Zafar, H., Lehmann, J., Maleshkova, M.: VQuAnDa: Verbalization QUestion ANswering DAtaset. e-prints (2019)
Liang, C., Berant, J., Le, Q., Forbus, K.D., Lao, N.: Neural symbolic machines: learning semantic parsers on freebase with weak supervision. arXiv e-prints (2016)
Lopez, V., Unger, C., Cimiano, P., Motta, E.: Evaluating question answering over linked data. Web Semant. Sci. Serv. Agents World Wide Web (2013). https://doi.org/10.1016/j.websem.2013.05.006
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. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017)
Mohammed, S., Shi, P., Lin, J.: Strong baselines for simple question answering over knowledge graphs with and without neural networks. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), New Orleans, Louisiana. Association for Computational Linguistics (2018)
Ngonga Ngomo, A.C., Bühmann, L., Unger, C., Lehmann, J., Gerber, D.: SPARQL2NL: verbalizing SPARQL queries. In: Proceedings of the 22nd International Conference on World Wide Web (2013)
Petrochuk, M., Zettlemoyer, L.: SimpleQuestions nearly solved: A new upperbound and baseline approach. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium. Association for Computational Linguistics (2018)
Saha, A., Pahuja, V., Khapra, M.M., Sankaranarayanan, K., Chandar, S.: Complex sequential question answering: towards learning to converse over linked question answer pairs with a knowledge graph. arXiv e-prints (2018)
Sun, Y., Tang, D., Duan, N., Gong, Y., Feng, X., Qin, B., Jiang, D.: Neural semantic parsing in low-resource settings with back-translation and meta-learning. arXiv e-prints (2019)
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
Zheng, W., Cheng, H., Zou, L., Yu, J.X., Zhao, K.: Natural language question/answering: Let users talk with the knowledge graph. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kacupaj, E. (2020). Explicable Question Answering. In: Harth, A., et al. The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020. Lecture Notes in Computer Science(), vol 12124. Springer, Cham. https://doi.org/10.1007/978-3-030-62327-2_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-62327-2_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62326-5
Online ISBN: 978-3-030-62327-2
eBook Packages: Computer ScienceComputer Science (R0)