Uma revisão breve sobre perguntas complexas em bases de conhecimento para sistemas de perguntas e respostas
Resumo
O avanço nos sistemas de Question Answering alcançou resultados importantes e novos problemas relacionados, como Complex Question Answering e Knowledge Base Question Answering, surgiram. No entanto, faltam estudos que analisam o problema e abordagens para Complex Knowledge Base Question Answering (C-KBQA). Este trabalho preenche essa lacuna apresentando uma visão geral do C-KBQA. Uma coleção de 54 artigos foi selecionada e um mapa dos métodos, abordagens, tendências e lacunas sobre C-KBQA foi realizado. É mostrado que as questões de múltiplos saltos e restritivas são os dois tipos de questões abordadas na literatura. Três etapas foram identificadas para criar um sistema C-KBQA e duas abordagens são geralmente usadas.
Referências
Agarwal, P., Ramanath, M., and Shroff, G. (2019). Retrieving relationships from a knowledge graph for question answering. In Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., and Hiemstra, D., editors, Advances in Information Retrieval, pages 35–50, Cham. Springer International Publishing.
Bao, J., Duan, N., Yan, Z., Zhou, M., and Zhao, T. (2016). Constraint-based question answering with knowledge graph. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2503–2514, Osaka, Japan. The COLING 2016 Organizing Committee.
Bhutani, N., Zheng, X., and Jagadish, H. V. (2019). Learning to answer complex questions over knowledge bases with query composition. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM ’19, page 739–748, New York, NY, USA. Association for Computing Machinery.
Bhutani, N., Zheng, X., Qian, K., Li, Y., and Jagadish, H. (2020). Answering complex questions by combining information from curated and extracted knowledge bases. In Proceedings of the First Workshop on Natural Language Interfaces, pages 1–10, Online. Association for Computational Linguistics.
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146.
Bordes, A., Usunier, N., Chopra, S., and Weston, J. (2015). Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075, abs/1506.02075.
Chen, X., Jia, S., and Xiang, Y. (2020). A review: Knowledge reasoning over knowledge graph. Expert Systems with Applications, 141:112948.
Croft, W. B., Metzler, D., and Strohman, T. (2010). Search engines: Information retrieval in practice, volume 520. Addison-Wesley Publishing, USA.
Cui, W., Xiao, Y., Wang, H., Song, Y., Hwang, S., and Wang, W. (2019). KBQA: CoRR, learning question answering over QA corpora and knowledge bases. abs/1903.02419:565–576.
da Silva, J. W. F., Venceslau, A. D. P., Sales, J. E., Maia, J. G. R., Pinheiro, V. C. M., and Vidal, V. M. P. (2020). A short survey on end-to-end simple question answering systems. Artificial Intelligence Review, 53(7):5429–5453.
Dettmers, T., Minervini, P., Stenetorp, P., and Riedel, S. (2018). Convolutional 2d knowledge graph embeddings. In McIlraith, S. A. and Weinberger, K. Q., editors, 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, New Orleans, Louisiana, USA. AAAI Press.
Dimitrakis, E., Sgontzos, K., and Tzitzikas, Y. (2019). A survey on question answering systems over linked data and documents. Journal of Intelligent Information Systems, 55:1–27.
Ding, J., Hu, W., Xu, Q., and Qu, Y. (2019). Leveraging frequent query substructures In Proceedings of the to generate formal queries for complex question answering. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2614–2622, Hong Kong, China. Association for Computational Linguistics.
Hao, Z., Wu, B., Wen, W., and Cai, R. (2019). A subgraph-representation-based method for answering complex questions over knowledge bases. Neural Networks, 119:57–65.
Höffner, K., Walter, S., Marx, E., Usbeck, R., Lehmann, J., and Ngonga Ngomo, A.-C. (2017). Survey on challenges of question answering in the semantic web. Semantic Web, 8(6):895–920.
Hu, S., Zou, L., and Zhang, X. (2018). A state-transition framework to answer complex questions over knowledge base. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2098–2108, Brussels, Belgium. Association for Computational Linguistics.
Hua, Y., Li, Y.-F., Haffari, G., Qi, G., and Wu, T. (2020a). Few-shot complex knowledge base question answering via meta reinforcement learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5827–5837, Online. Association for Computational Linguistics.
Hua, Y., Li, Y.-F., Haffari, G., Qi, G., and Wu, W. (2020b). Retrieve, program, repeat: In BesComplex knowledge base question answering via alternate meta-learning. siere, C., editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 3679–3686, Virtual, Japan. International Joint Conferences on Artificial Intelligence Organization. Main track.
Ji, S., Pan, S., Cambria, E., Marttinen, P., and Yu, P. S. (2020). A survey on knoarXiv preprint arwledge graphs: Representation, acquisition and applications. Xiv:2002.00388, abs/2002.00388.
Jia, R. and Liang, P. (2017). Adversarial examples for evaluating reading comprehension In Proceedings of the 2017 Conference on Empirical Methods in Natural systems. Language Processing, pages 2021–2031, Vancouver, Canada. Association for Computational Linguistics.
Kartsaklis, D., Pilehvar, M. T., and Collier, N. (2018). Mapping text to knowledge graph entities using multi-sense LSTMs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1959–1970, Brussels, Belgium. Association for Computational Linguistics.
Liu, Y., Wan, Y., He, L., Peng, H., and Yu, P. S. (2020). Kg-bart: Knowledge graphaugmented bart for generative commonsense reasoning.
Lukovnikov, D., Fischer, A., and Lehmann, J. (2019). Pretrained transformers for simple In International Semantic Web Confequestion answering over knowledge graphs. rence, volume abs/2001.11985, pages 470–486. Springer.
Luo, K., Lin, F., Luo, X., and Zhu, K. (2018). Knowledge base question answering via encoding of complex query graphs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2185–2194, Brussels, Belgium. Association for Computational Linguistics.
McCoy, R. T., Min, J., and Linzen, T. (2019). Berts of a feather do not generalize together: Large variability in generalization across models with similar test set performance. arXiv preprint arXiv:1911.02969, abs/1911.02969:217–227.
Mendes, P. N., Jakob, M., García-Silva, A., and Bizer, C. (2011). Dbpedia spotlight: shedding light on the web of documents. In Proceedings of the 7th international conference on semantic systems, pages 1–8, New York, NY, USA. Association for Computing Machinery.
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
Miller, A., Fisch, A., Dodge, J., Karimi, A.-H., Bordes, A., and Weston, J. (2016). Keyvalue memory networks for directly reading documents. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1400–1409, Austin, Texas. Association for Computational Linguistics.
Namaki, M. H., Chowdhury, F. A., Islam, M., Doppa, J., and Wu, Y. (2017). Learning to speed up query planning in graph databases. Proceedings of the International Conference on Automated Planning and Scheduling, 27(1):9.
Neiva, F. W., David, J. M. N., Braga, R., and Campos, F. (2016). Towards pragmatic interoperability to support collaboration: A systematic review and mapping of the literature. Information and Software Technology, 72:137–150.
Pennington, J., Socher, R., and Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543, Doha, Qatar. Association for Computational Linguistics.
Petersen, K., Vakkalanka, S., and Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1–18.
Petticrew, M. and Roberts, H. (2006). Systematic reviews in the social sciences: a practical guide. 2006. Malden USA: Blackwell Publishing CrossRef Google Scholar, 6:304– 305.
Rodrigo, A. and Penas, A. (2017). A study about the future evaluation of questionanswering systems. Knowledge-Based Systems, 137:83–93.
Saha, A., Pahuja, V., Khapra, M., Sankaranarayanan, K., and Chandar, S. (2018). Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1):9.
Schumacher, E., Mayfield, J., and Dredze, M. (2020). Cross-lingual transfer in zero-shot cross-language entity linking.
Shin, S. and Lee, K.-H. (2020). Processing knowledge graph-based complex questions through question decomposition and recomposition. Information Sciences, 523:234– 244.
Tong, P., Zhang, Q., and Yao, J. (2019). Leveraging domain context for question answering over knowledge graph. Data Science and Engineering, 4(4):323–335.
Trivedi, P., Maheshwari, G., Dubey, M., and Lehmann, J. (2017). Lc-quad: A corpus for complex question answering over knowledge graphs. In International Semantic Web Conference, pages 210–218, Cham. Springer, Springer International Publishing.
Wu, L., Wu, P., and Zhang, X. (2019a). A seq2seq-based approach to question answering over knowledge bases. In Joint International Semantic Technology Conference, pages 170–181, Singapore. Springer, Springer Singapore.
Wu, P., Zhang, X., and Feng, Z. (2019b). A survey of question answering over knowledge base. In China Conference on Knowledge Graph and Semantic Computing, pages 86– 97, Singapore. Springer, Springer Singapore.
Yang, Y. and Chang, M.-W. (2015). 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 and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 504–513, Beijing, China. Association for Computational Linguistics.
Yin, W., Ge, W., and Wang, H. (2014). Cdqa: An ontology-based question answering In 2014 IEEE 3rd International Conference on Cloud system for chinese delicacy. Computing and Intelligence Systems, pages 1–7, Shenzhen, China. IEEE.
Zhang, Y., Dai, H., Kozareva, Z., Smola, A., and Song, L. (2018). Variational reasoning for question answering with knowledge graph. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1):6069–6076.
Zheng, C. and Kordjamshidi, P. (2020). SRLGRN: Semantic role labeling graph reaIn Proceedings of the 2020 Conference on Empirical Methods in soning network. Natural Language Processing (EMNLP), pages 8881–8891, Online. Association for Computational Linguistics.
Bao, J., Duan, N., Yan, Z., Zhou, M., and Zhao, T. (2016). Constraint-based question answering with knowledge graph. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2503–2514, Osaka, Japan. The COLING 2016 Organizing Committee.
Bhutani, N., Zheng, X., and Jagadish, H. V. (2019). Learning to answer complex questions over knowledge bases with query composition. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM ’19, page 739–748, New York, NY, USA. Association for Computing Machinery.
Bhutani, N., Zheng, X., Qian, K., Li, Y., and Jagadish, H. (2020). Answering complex questions by combining information from curated and extracted knowledge bases. In Proceedings of the First Workshop on Natural Language Interfaces, pages 1–10, Online. Association for Computational Linguistics.
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146.
Bordes, A., Usunier, N., Chopra, S., and Weston, J. (2015). Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075, abs/1506.02075.
Chen, X., Jia, S., and Xiang, Y. (2020). A review: Knowledge reasoning over knowledge graph. Expert Systems with Applications, 141:112948.
Croft, W. B., Metzler, D., and Strohman, T. (2010). Search engines: Information retrieval in practice, volume 520. Addison-Wesley Publishing, USA.
Cui, W., Xiao, Y., Wang, H., Song, Y., Hwang, S., and Wang, W. (2019). KBQA: CoRR, learning question answering over QA corpora and knowledge bases. abs/1903.02419:565–576.
da Silva, J. W. F., Venceslau, A. D. P., Sales, J. E., Maia, J. G. R., Pinheiro, V. C. M., and Vidal, V. M. P. (2020). A short survey on end-to-end simple question answering systems. Artificial Intelligence Review, 53(7):5429–5453.
Dettmers, T., Minervini, P., Stenetorp, P., and Riedel, S. (2018). Convolutional 2d knowledge graph embeddings. In McIlraith, S. A. and Weinberger, K. Q., editors, 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, New Orleans, Louisiana, USA. AAAI Press.
Dimitrakis, E., Sgontzos, K., and Tzitzikas, Y. (2019). A survey on question answering systems over linked data and documents. Journal of Intelligent Information Systems, 55:1–27.
Ding, J., Hu, W., Xu, Q., and Qu, Y. (2019). Leveraging frequent query substructures In Proceedings of the to generate formal queries for complex question answering. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2614–2622, Hong Kong, China. Association for Computational Linguistics.
Hao, Z., Wu, B., Wen, W., and Cai, R. (2019). A subgraph-representation-based method for answering complex questions over knowledge bases. Neural Networks, 119:57–65.
Höffner, K., Walter, S., Marx, E., Usbeck, R., Lehmann, J., and Ngonga Ngomo, A.-C. (2017). Survey on challenges of question answering in the semantic web. Semantic Web, 8(6):895–920.
Hu, S., Zou, L., and Zhang, X. (2018). A state-transition framework to answer complex questions over knowledge base. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2098–2108, Brussels, Belgium. Association for Computational Linguistics.
Hua, Y., Li, Y.-F., Haffari, G., Qi, G., and Wu, T. (2020a). Few-shot complex knowledge base question answering via meta reinforcement learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5827–5837, Online. Association for Computational Linguistics.
Hua, Y., Li, Y.-F., Haffari, G., Qi, G., and Wu, W. (2020b). Retrieve, program, repeat: In BesComplex knowledge base question answering via alternate meta-learning. siere, C., editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 3679–3686, Virtual, Japan. International Joint Conferences on Artificial Intelligence Organization. Main track.
Ji, S., Pan, S., Cambria, E., Marttinen, P., and Yu, P. S. (2020). A survey on knoarXiv preprint arwledge graphs: Representation, acquisition and applications. Xiv:2002.00388, abs/2002.00388.
Jia, R. and Liang, P. (2017). Adversarial examples for evaluating reading comprehension In Proceedings of the 2017 Conference on Empirical Methods in Natural systems. Language Processing, pages 2021–2031, Vancouver, Canada. Association for Computational Linguistics.
Kartsaklis, D., Pilehvar, M. T., and Collier, N. (2018). Mapping text to knowledge graph entities using multi-sense LSTMs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1959–1970, Brussels, Belgium. Association for Computational Linguistics.
Liu, Y., Wan, Y., He, L., Peng, H., and Yu, P. S. (2020). Kg-bart: Knowledge graphaugmented bart for generative commonsense reasoning.
Lukovnikov, D., Fischer, A., and Lehmann, J. (2019). Pretrained transformers for simple In International Semantic Web Confequestion answering over knowledge graphs. rence, volume abs/2001.11985, pages 470–486. Springer.
Luo, K., Lin, F., Luo, X., and Zhu, K. (2018). Knowledge base question answering via encoding of complex query graphs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2185–2194, Brussels, Belgium. Association for Computational Linguistics.
McCoy, R. T., Min, J., and Linzen, T. (2019). Berts of a feather do not generalize together: Large variability in generalization across models with similar test set performance. arXiv preprint arXiv:1911.02969, abs/1911.02969:217–227.
Mendes, P. N., Jakob, M., García-Silva, A., and Bizer, C. (2011). Dbpedia spotlight: shedding light on the web of documents. In Proceedings of the 7th international conference on semantic systems, pages 1–8, New York, NY, USA. Association for Computing Machinery.
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
Miller, A., Fisch, A., Dodge, J., Karimi, A.-H., Bordes, A., and Weston, J. (2016). Keyvalue memory networks for directly reading documents. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1400–1409, Austin, Texas. Association for Computational Linguistics.
Namaki, M. H., Chowdhury, F. A., Islam, M., Doppa, J., and Wu, Y. (2017). Learning to speed up query planning in graph databases. Proceedings of the International Conference on Automated Planning and Scheduling, 27(1):9.
Neiva, F. W., David, J. M. N., Braga, R., and Campos, F. (2016). Towards pragmatic interoperability to support collaboration: A systematic review and mapping of the literature. Information and Software Technology, 72:137–150.
Pennington, J., Socher, R., and Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543, Doha, Qatar. Association for Computational Linguistics.
Petersen, K., Vakkalanka, S., and Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1–18.
Petticrew, M. and Roberts, H. (2006). Systematic reviews in the social sciences: a practical guide. 2006. Malden USA: Blackwell Publishing CrossRef Google Scholar, 6:304– 305.
Rodrigo, A. and Penas, A. (2017). A study about the future evaluation of questionanswering systems. Knowledge-Based Systems, 137:83–93.
Saha, A., Pahuja, V., Khapra, M., Sankaranarayanan, K., and Chandar, S. (2018). Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1):9.
Schumacher, E., Mayfield, J., and Dredze, M. (2020). Cross-lingual transfer in zero-shot cross-language entity linking.
Shin, S. and Lee, K.-H. (2020). Processing knowledge graph-based complex questions through question decomposition and recomposition. Information Sciences, 523:234– 244.
Tong, P., Zhang, Q., and Yao, J. (2019). Leveraging domain context for question answering over knowledge graph. Data Science and Engineering, 4(4):323–335.
Trivedi, P., Maheshwari, G., Dubey, M., and Lehmann, J. (2017). Lc-quad: A corpus for complex question answering over knowledge graphs. In International Semantic Web Conference, pages 210–218, Cham. Springer, Springer International Publishing.
Wu, L., Wu, P., and Zhang, X. (2019a). A seq2seq-based approach to question answering over knowledge bases. In Joint International Semantic Technology Conference, pages 170–181, Singapore. Springer, Springer Singapore.
Wu, P., Zhang, X., and Feng, Z. (2019b). A survey of question answering over knowledge base. In China Conference on Knowledge Graph and Semantic Computing, pages 86– 97, Singapore. Springer, Springer Singapore.
Yang, Y. and Chang, M.-W. (2015). 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 and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 504–513, Beijing, China. Association for Computational Linguistics.
Yin, W., Ge, W., and Wang, H. (2014). Cdqa: An ontology-based question answering In 2014 IEEE 3rd International Conference on Cloud system for chinese delicacy. Computing and Intelligence Systems, pages 1–7, Shenzhen, China. IEEE.
Zhang, Y., Dai, H., Kozareva, Z., Smola, A., and Song, L. (2018). Variational reasoning for question answering with knowledge graph. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1):6069–6076.
Zheng, C. and Kordjamshidi, P. (2020). SRLGRN: Semantic role labeling graph reaIn Proceedings of the 2020 Conference on Empirical Methods in soning network. Natural Language Processing (EMNLP), pages 8881–8891, Online. Association for Computational Linguistics.
Publicado
29/11/2021
Como Citar
GOMES JR., Jorão; MELLO, Rômulo Chrispim de; REIS, Ana Beatriz Kapps dos; STRÖELE, Victor; SOUZA, Jairo Francisco de.
Uma revisão breve sobre perguntas complexas em bases de conhecimento para sistemas de perguntas e respostas. In: SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E DA LINGUAGEM HUMANA (STIL), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 283-294.
DOI: https://doi.org/10.5753/stil.2021.17808.
