publication . Preprint . 2018

Variational Knowledge Graph Reasoning

Chen, Wenhu; Xiong, Wenhan; Yan, Xifeng; Wang, William;
Open Access English
  • Published: 17 Mar 2018
Abstract
Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we...
Subjects
free text keywords: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
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24 references, page 1 of 2

Antoine Bordes, Nicolas Usunier, Alberto GarciaDuran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multirelational data. In Advances in neural information processing systems. pages 2787-2795. [OpenAIRE]

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. 2017. Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning. arXiv preprint arXiv:1711.05851 . [OpenAIRE]

Rajarshi Das, Arvind Neelakantan, David Belanger, and Andrew McCallum. 2016. Chains of reasoning over entities, relations, and text using recurrent neural networks. arXiv preprint arXiv:1607.01426 . [OpenAIRE]

Matt Gardner, Partha Pratim Talukdar, Bryan Kisiel, and Tom Mitchell. 2013. Improving learning and inference in a large knowledge-base using latent syntactic cues .

Matt Gardner, Partha Pratim Talukdar, Jayant Krishnamurthy, and Tom Mitchell. 2014. Incorporating vector space similarity in random walk inference over knowledge bases .

Kelvin Guu, Tatsunori B Hashimoto, Yonatan Oren, and Percy Liang. 2017a. Generating sentences by editing prototypes. arXiv preprint arXiv:1709.08878 . [OpenAIRE]

Kelvin Guu, Panupong Pasupat, Evan Zheran Liu, and Percy Liang. 2017b. From language to programs: Bridging reinforcement learning and maximum marginal likelihood. arXiv preprint arXiv:1704.07926 . [OpenAIRE]

Sepp Hochreiter and Ju¨rgen Schmidhuber. 1997. Long short-term memory. Neural computation 9(8):1735-1780.

Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge graph embedding via dynamic mapping matrix. In ACL (1). pages 687-696.

Diederik P Kingma and Max Welling. 2013. Autoencoding variational bayes. arXiv preprint arXiv:1312.6114 .

Ni Lao, Tom Mitchell, and William W Cohen. 2011. Random walk inference and learning in a large scale knowledge base. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 529-539.

Yann LeCun, Yoshua Bengio, et al. 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361(10):1995.

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In AAAI. pages 2181-2187.

Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov. 2015. Generating images from captions with attention. arXiv preprint arXiv:1511.02793 . [OpenAIRE]

Arvind Neelakantan, Benjamin Roth, and Andrew McCallum. 2015. Compositional vector space models for knowledge base inference. In 2015 aaai spring symposium series. [OpenAIRE]

24 references, page 1 of 2
Related research
Abstract
Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we...
Subjects
free text keywords: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Download from
24 references, page 1 of 2

Antoine Bordes, Nicolas Usunier, Alberto GarciaDuran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multirelational data. In Advances in neural information processing systems. pages 2787-2795. [OpenAIRE]

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. 2017. Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning. arXiv preprint arXiv:1711.05851 . [OpenAIRE]

Rajarshi Das, Arvind Neelakantan, David Belanger, and Andrew McCallum. 2016. Chains of reasoning over entities, relations, and text using recurrent neural networks. arXiv preprint arXiv:1607.01426 . [OpenAIRE]

Matt Gardner, Partha Pratim Talukdar, Bryan Kisiel, and Tom Mitchell. 2013. Improving learning and inference in a large knowledge-base using latent syntactic cues .

Matt Gardner, Partha Pratim Talukdar, Jayant Krishnamurthy, and Tom Mitchell. 2014. Incorporating vector space similarity in random walk inference over knowledge bases .

Kelvin Guu, Tatsunori B Hashimoto, Yonatan Oren, and Percy Liang. 2017a. Generating sentences by editing prototypes. arXiv preprint arXiv:1709.08878 . [OpenAIRE]

Kelvin Guu, Panupong Pasupat, Evan Zheran Liu, and Percy Liang. 2017b. From language to programs: Bridging reinforcement learning and maximum marginal likelihood. arXiv preprint arXiv:1704.07926 . [OpenAIRE]

Sepp Hochreiter and Ju¨rgen Schmidhuber. 1997. Long short-term memory. Neural computation 9(8):1735-1780.

Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge graph embedding via dynamic mapping matrix. In ACL (1). pages 687-696.

Diederik P Kingma and Max Welling. 2013. Autoencoding variational bayes. arXiv preprint arXiv:1312.6114 .

Ni Lao, Tom Mitchell, and William W Cohen. 2011. Random walk inference and learning in a large scale knowledge base. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 529-539.

Yann LeCun, Yoshua Bengio, et al. 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361(10):1995.

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In AAAI. pages 2181-2187.

Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov. 2015. Generating images from captions with attention. arXiv preprint arXiv:1511.02793 . [OpenAIRE]

Arvind Neelakantan, Benjamin Roth, and Andrew McCallum. 2015. Compositional vector space models for knowledge base inference. In 2015 aaai spring symposium series. [OpenAIRE]

24 references, page 1 of 2
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