@inproceedings{aglionby-teufel-2022-identifying,
title = "Identifying relevant common sense information in knowledge graphs",
author = "Aglionby, Guy and
Teufel, Simone",
booktitle = "Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.csrr-1.1",
doi = "10.18653/v1/2022.csrr-1.1",
pages = "1--7",
abstract = "Knowledge graphs are often used to store common sense information that is useful for various tasks. However, the extraction of contextually-relevant knowledge is an unsolved problem, and current approaches are relatively simple. Here we introduce a triple selection method based on a ranking model and find that it improves question answering accuracy over existing methods. We additionally investigate methods to ensure that extracted triples form a connected graph. Graph connectivity is important for model interpretability, as paths are frequently used as explanations for the reasoning that connects question and answer.",
}
Markdown (Informal)
[Identifying relevant common sense information in knowledge graphs](https://aclanthology.org/2022.csrr-1.1) (Aglionby & Teufel, CSRR 2022)
ACL