Abstract
Providing machines with the capability of exploring knowledge graphs and answering natural language questions has been an active area of research over the past decade. In this direction translating natural language questions to formal queries has been one of the key approaches. To advance the research area, several datasets like WebQuestions, QALD and LCQuAD have been published in the past. The biggest data set available for complex questions (LCQuAD) over knowledge graphs contains five thousand questions. We now provide LC-QuAD 2.0 (Large-Scale Complex Question Answering Dataset) with 30,000 questions, their paraphrases and their corresponding SPARQL queries. LC-QuAD 2.0 is compatible with both Wikidata and DBpedia 2018 knowledge graphs. In this article, we explain how the dataset was created and the variety of questions available with examples. We further provide a statistical analysis of the dataset.
Resource Type: Dataset
Website and documentation: http://lc-quad.sda.tech/
Permanent URL: https://figshare.com/projects/LCQuAD_2_0/62270.
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Notes
- 1.
We refer this as ’DBpedia2018’ further in this article.
- 2.
Qualifiers are used in order to further describe or refine the value of a property given in a fact statement: https://www.wikidata.org/wiki/Help:Qualifiers.
- 3.
at the time of writing this article, these updates do not reflect on the public DBpedia end-point. Authors have hosted a local endpoint of their own (using data from http://downloads.dbpedia.org/repo/lts/wikidata/). In future the authors shall release their own endpoint point with the new DBpedia model.
- 4.
References
Berant, J., Liang, P.: Semantic parsing via paraphrasing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1415–1425 (2014)
Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks. CoRR, abs/1506.02075 (2015)
Cai, Q., Yates, A.: Large-scale semantic parsing via schema matching and lexicon extension. In: ACL, pp.423–433 (2013)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805 (2018)
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
Dubey, M., Dasgupta, S., Sharma, A., Höffner, K., Lehmann, J.: AskNow: A framework for natural language query formalization in SPARQL. In: International Semantic Web Conference, pp. 300–316 (2016)
Ismayilov, A., Kontokostas, D., Auer, S., Lehmann, J., Hellmann, S., et al.: Wikidata through the eyes of DBpedia. Semant. Web 9(4), 493–503 (2018)
Lehmann, J., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. The Semantic Web, pp. 167–195 (2015)
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 World Wide Web Conference, pp. 1211–1220 (2017)
Choi, K.S., et al. (eds.): 9th Question Answering over Linked Data challenge (QALD-9) co-located with 17th International Semantic Web Conference, Monterey, California, United States of America, CEUR Workshop Proceedings, CEUR-WS.org, vol. 2241 (2018). https://dblp.org/rec/bib/conf/semweb/2018semdeep
Serban, I.V., et al.: Generating factoid questions with recurrent neural networks: the 30M factoid question-answer corpus. In: 54th Annual Meeting of the Association for Computational Linguistics (2016)
Talmor, A., Berant, J.: The web as a knowledge-base for answering complex questions. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 641–651 (2018)
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
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledge base (2014)
Yih, W.-T., Chang, M.-W., He, X., Gao, J.: Semantic parsing via staged query graph generation: question answering with knowledge base. In: Proceedings of the 53rd Annual Meeting of the ACL and the 7th International Joint Conference on NLP (2015)
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
Acknowledgements
This work has mainly been supported by the Fraunhofer-Cluster of Excellence “Cognitive Internet Technologies” (CCIT). It has also partly been supported by the German Federal Ministry of Education and Research (BMBF) in the context of the research project “InclusiveOCW” (grant no. 01PE17004D).
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Dubey, M., Banerjee, D., Abdelkawi, A., Lehmann, J. (2019). LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11779. Springer, Cham. https://doi.org/10.1007/978-3-030-30796-7_5
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