Evolutionary Learning of Existential Rules

IJCAI 2019 pp. 6478-6479

doi:10.24963/IJCAI.2019/928 /ijcai/2019/wu2019ijcai-evolutionary/

Abstract

Declarative rules such as Prolog and Datalog are common formalisms to express expert knowledge and are used in a number of systems. Since developing such rules is time-consuming and requires scarce expert knowledge, it is essential to develop algorithms for learning such rules. We address the problem of learning existential rules, a richer class of rules which found applications in many use-cases such as Semantic Web and Web Data Extraction. In particular, we concentrate on developing evolutionary learning algorithms for rule learning.

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Cite

Text

Wu. "Evolutionary Learning of Existential Rules." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/928

Markdown

[Wu. "Evolutionary Learning of Existential Rules." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/wu2019ijcai-evolutionary/) doi:10.24963/IJCAI.2019/928

BibTeX

@inproceedings{wu2019ijcai-evolutionary,
  title     = {{Evolutionary Learning of Existential Rules}},
  author    = {Wu, Lianlong},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {6478-6479},
  doi       = {10.24963/IJCAI.2019/928},
  url       = {https://mlanthology.org/ijcai/2019/wu2019ijcai-evolutionary/}
}