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preprint · arXiv (Cornell University) · 2016

Probabilistic Knowledge Graph Construction

Dongwoo Kim, Lexing Xie, Cheng Soon Ong

Cited by 11Open access

Abstract

Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinder the subsequent statistical inference. The gap between these two processes can be reduced by an incremental population approach. We propose a new probabilistic knowledge graph factorisation method that benefits from the path structure of existing knowledge (e.g. syllogism) and enables a common modelling approach to be used for both incremental population and knowledge completion tasks. More specifically, the probabilistic formulation allows us to develop an incremental population algorithm that trades off exploitation-exploration. Experiments on three benchmark datasets show that the balanced exploitation-exploration helps the incremental population, and the additional path structure helps to predict missing information in knowledge completion.

Cite this paper

APA 7

Kim, D., Xie, L., & Ong, C. S. (2016). Probabilistic Knowledge Graph Construction. arXiv (Cornell University), 2257–2262. https://doi.org/10.1145/2983323.2983677

MLA 9

Kim, Dongwoo, et al. “Probabilistic Knowledge Graph Construction.” arXiv (Cornell University), 2016, pp. 2257–62, https://doi.org/10.1145/2983323.2983677.

Chicago

Kim, Dongwoo, Lexing Xie, and Cheng Soon Ong. 2016. “Probabilistic Knowledge Graph Construction.” arXiv (Cornell University): 2257–62. https://doi.org/10.1145/2983323.2983677.

Harvard

Kim, D., Xie, L. and Ong, C.S. (2016) ‘Probabilistic Knowledge Graph Construction’, arXiv (Cornell University), pp. 2257–2262. Available at: https://doi.org/10.1145/2983323.2983677.

BibTeX
@misc{kim2016probabilistic,
  title = {{Probabilistic Knowledge Graph Construction}},
  author = {Kim, Dongwoo and Xie, Lexing and Ong, Cheng Soon},
  journal = {arXiv (Cornell University)},
  pages = {2257--2262},
  year = {2016},
  doi = {10.1145/2983323.2983677}
}

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