ML Anthology

A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations

Kangqi Luo, Xusheng Luo, Xianyang Chen, Kenny Q. Zhu
IJCAI 2017 pp. 1174-1180
doi:10.24963/IJCAI.2017/163 /ijcai/2017/luo2017ijcai-data/

Abstract

This paper studies the problem of discovering the structured knowledge representation of binary natural language relations. The representation, known as the schema, generalizes the traditional path of predicates to support more complex semantics. We present a search algorithm to generate schemas over a knowledge base, and propose a data-driven learning approach to discover the most suitable representations to one relation. Evaluation results show that inferred schemas are able to represent precise semantics, and can be used to enrich manually crafted knowledge bases.

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Text

Luo et al. "A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/163

Markdown

[Luo et al. "A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/luo2017ijcai-data/) doi:10.24963/IJCAI.2017/163

BibTeX

@inproceedings{luo2017ijcai-data,
  title     = {{A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations}},
  author    = {Luo, Kangqi and Luo, Xusheng and Chen, Xianyang and Zhu, Kenny Q.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {1174-1180},
  doi       = {10.24963/IJCAI.2017/163},
  url       = {https://mlanthology.org/ijcai/2017/luo2017ijcai-data/}
}