-
摘要: 面向知识库的问答(Question answering over knowledge base, KBQA)是问答系统的重要组成. 近些年, 随着以深度学习为代表的表示学习技术在多个领域的成功应用, 许多研究者开始着手研究基于表示学习的知识库问答技术. 其基本假设是把知识库问答看做是一个语义匹配的过程. 通过表示学习知识库以及用户问题的语义表示, 将知识库中的实体、关系以及问句文本转换为一个低维语义空间中的数值向量, 在此基础上, 利用数值计算, 直接匹配与用户问句语义最相似的答案. 从目前的结果看, 基于表示学习的知识库问答系统在性能上已经超过传统知识库问答方法. 本文将对现有基于表示学习的知识库问答的研究进展进行综述, 包括知识库表示学习和问句(文本)表示学习的代表性工作, 同时对于其中存在难点以及仍存在的研究问题进行分析和讨论.Abstract: Question answering over knowledge base (KBQA) is an important direction for the research of question answering. Recently, with the drastic development of deep learning, researchers and developers have paid more attentions to KBQA from this angle. They regarded this problem as a task of semantic matching. The semantics of knowledge base and users' questions are learned through representation learning under the framework of deep learning. The entities and relations in knowledge base and the texts in questions could be represented as numerical vectors. Then, the answer could be figured out through similarity computation between the vectors of knowledge base and the vectors of the given question. From reported results, KBQA based on representation learning has obtained the best performance. This paper introduces the mainstream methods in this area. It further induces the typical approaches of representation learning on knowledge base and texts (questions), respectively. Finally, the current research challenges are discussed.
-
图 2 基于表示学习的知识库问答方法与传统方法的性能比较
Fig. 2 The comparisons between representation learning based KBQA and traditional KBQA
-
[1] Etzioni O. Search needs a shake-up. Nature, 2011, 476(7358) : 25-26 [2] Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes P N, Hellmann S, Morsey M, van Kleef P, Auer S, Bizer C. Dbpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web, 2015, 6(2) : 167-195 [3] Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J. Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. Vancouver, Canada: ACM, 2008. 1247-1250 [4] Suchanek F M, Kasneci G, Weikum G. Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web. Alberta, Canada: ACM, 2007. 697-706 [5] Kwiatkowski T, Zettlemoyer L, Goldwater S, Steedman M. Lexical generalization in CCG grammar induction for semantic parsing. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Scotland, UK: Association for Computational Linguistics, 2011. 1512-1523 [6] Liang P, Jordan M I, Klein D. Learning dependency-based compositional semantics. Computational Linguistics, 2013, 39(2) : 389-446 [7] Socher R, Perelygin A, Wu J Y, Chuang J, Manning C D, Ng A Y, Potts C. Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Seattle, USA: Association for Computational Linguistics, 2013. 1631-1642 [8] Cho K, Van Merriënboer B, Gülçehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha, Qatar: Association for Computational Linguistics, 2014. 1724-1734 [9] Socher R, Manning C D, Ng A Y. Learning continuous phrase representations and syntactic parsing with recursive neural networks. In: Proceedings of the NIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop. Vancouver, Canada, 2010. 1-9 [10] Bordes A, Weston J, Usunier N. Open question answering with weakly supervised embedding models. Machine Learning and Knowledge Discovery in Databases. Berlin Heidelberg: Springer, 2014. 165-180 [11] Bordes A, Chopra S, Weston J. Question answering with subgraph embeddings. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha, Qatar: Association for Computational Linguistic, 2014. 615-620 [12] Dong L, Wei F R, 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. Beijing, China: ACL, 2015. 260-269 [13] Nickel M, Tresp V, Kriegel H P. A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning. Washington, USA, 2011. 809-816 [14] Nickel M, Tresp V, Kriegel H P. Factorizing YAGO: scalable machine learning for linked data. In: Proceedings of the 21st International Conference on World Wide Web. Lyon, France: ACM, 2012. 271-280 [15] Nickel M, Tresp V. Logistic tensor factorization for multi-relational data. In: Proceedings of the 30th International Conference on Machine Learning. Atlanta, USA, 2013. [16] Nickel M, Tresp V. Tensor factorization for multi-relational learning. Machine Learning and Knowledge Discovery in Databases. Berlin Heidelberg: Springer, 2013. 617-621 [17] Nickel M, Murphy K, Tresp V, Gabrilovich E. A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 2016, 104(1) : 11-33 [18] Bordes A, Weston J, Collobert R, Bengio Y. Learning structured embeddings of knowledge bases. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence. San Francisco, USA: AAAI, 2011. 301-306 [19] Bordes A, Glorot X, Weston J, Bengio Y. Joint learning of words and meaning representations for open-text semantic parsing. In: Proceedings of the 15th International Conference on Artificial Intelligence and Statistics. Cadiz, Spain, 2012. 127-135 [20] Bordes A, Glorot X, Weston J, Bengio Y. A semantic matching energy function for learning with multi-relational data. Machine Learning, 2014, 94(2) : 233-259 [21] Jenatton R, Le Roux N, Bordes A, Obozinski G. A latent factor model for highly multi-relational data. In: Proceedings of Advances in Neural Information Processing Systems 25. Lake Tahoe, Nevada, United States: Curran Associates, Inc., 2012. 3167-3175 [22] Sutskever I, Salakhutdinov R, Joshua B T. Modelling relational data using Bayesian clustered tensor factorization. In: Proceedings of Advances in Neural Information Processing Systems 22. Vancouver, Canada, 2009. 1821-1828 [23] Socher R, Chen D Q, Manning C D, Ng A Y. Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of Advances in Neural Information Processing Systems 26. Stateline, USA: MIT Press, 2013. 926-934 [24] Bordes A, Usunier N, Garcia-Durán A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In: Proceedings of Advances in Neural Information Processing Systems 26. Stateline, USA: MIT Press, 2013. 2787-2795 [25] Wang Z, Zhang J W, Feng J L, Chen Z. Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec, Canada: AAAI, 2014. 1112-1119 [26] Lin Y, Zhang J, Liu Z, Sun M, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. Austin, USA: AAAI, 2015. 2181-2187 [27] Ji G L, He S Z, Xu L H, Liu K, Zhao J. Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. Beijing, China: Association for Computational Linguistics, 2015. 687-696 [28] Lin Y K, Liu Z Y, Luan H B, Sun M S, Rao S W, Liu S. Modeling relation paths for representation learning of knowledge bases. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal: Association for Computational Linguistics, 2015. 705-714 [29] Guo S, Wang Q, Wang B, Wang L H, Guo L. Semantically smooth knowledge graph embedding. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Beijing, China: Association for Computational Linguistics, 2015. 84-94 [30] He S Z, Liu K, Ji G L, Zhao J. Learning to represent knowledge graphs with Gaussian embedding. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne, Australia: ACM, 2015. 623-632. [31] Frege G. Jber sinn und bedeutung. Wittgenstein Studien, 1892, 100: 25-50 [32] Hermann K M. Distributed Representations for Compositional Semantics[Ph.D. dissertation], University of Oxford, 2014 [33] Socher R, Karpathy A, Le Q V, Manning C D, Ng A Y. Grounded compositional semantics for finding and describing images with sentences. Transactions of the Association for Computational Linguistics, 2014, 2: 207-218 [34] Socher R, Huang E H, Pennin J, Manning C D, Ng A Y. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Proceedings of Advances in Neural Information Processing Systems 24. Granada, Spain, 2011. 801-809 [35] Socher R, Pennington J, Huang E H, Ng A Y, Manning C D. Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Scotland, UK: Association for Computational Linguistics, 2011. 151-161 [36] Socher R, Lin C C Y, Ng A Y, Manning C D. Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th International Conference on Machine Learning. Bellevue, WA, USA, 2011. 129-136 [37] Socher R, Bauer J, Manning C D, Ng A Y. Parsing with compositional vector grammars. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Sofia, Bulgaria: Association for Computational Linguistics, 2013. 455-465 [38] Mitchell J, Lapata M. Composition in distributional models of semantics. Cognitive Science, 2010, 34(8) : 1388-1429 [39] Socher R, Huval B, Manning C D, Ng A Y. Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Jeju Island, Korea: Association for Computational Linguistics, 2012. 1201-1211 [40] Elman J L. Finding structure in time. Cognitive Science, 1990, 14(2) : 179-211 [41] Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 1994, 5(2) : 157-166 [42] Hochreiter S. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1998, 6(2) : 107-116 [43] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8) : 1735-1780 [44] Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 1980, 36(4) : 193-202 [45] LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11) : 2278-2324 [46] Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 2011, 12: 2493-2537 [47] Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore, USA: Association for Computational Linguistics, 2014. 655-665 [48] Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha, Qatar: Association for Computational Linguistics, 2014. 1746-1751 [49] Zeng D H, Liu K, Lai S W, Zhou G Y, Zhao J. Relation classification via convolutional deep neural network. In: Proceedings of the 25th International Conference on Computational Linguistics. Dublin, Ireland: Association for Computational Linguistics, 2014. 2335-2344 [50] Fader A, Soderland S, Etzioni O. Identifying relations for open information extraction. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Scotland, UK: Association for Computational Linguistics, 2011. 1535-1545 [51] Yih W T, He X D, Meek C. Semantic parsing for single-relation question answering. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistic. Baltimore, USA: Association for Computational Linguistic, 2014. 643-648 [52] Fader A, Zettlemoyer L S, Etzioni O. Paraphrase-driven learning for open question answering. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Sofia, Bulgaria: Association for Computational Linguistics, 2013. 1608-1618 [53] Berant J, Liang P. Semantic parsing via paraphrasing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistic. Baltimore, USA: Association for Computational Linguistic, 2014. 1415-1425 [54] Yao X C, Van Durme B. Information extraction over structured data: question answering with freebase. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistic. Baltimore, USA: Association for Computational Linguistic, 2014. 956-966 [55] Zhao H, Lu Z D, Poupart P. Self-adaptive hierarchical sentence model. In: Proceedings of the 24th International Conference on Artificial Intelligence. Buenos Aires, Argentina: AAAI, 2015. 4069-4076 [56] Luong M T, Pham H, Manning C D. Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal: ACL, 2015. 1412-1421 [57] Fader A, Zettlemoyer L, Etzioni O. Open question answering over curated and extracted knowledge bases. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2014. 1156-1165 -
下载:
图(11)
计量
- 文章访问数: 6841
- HTML全文浏览量: 1792
- PDF下载量: 4584
- 被引次数: 0
