Commonsense Knowledge Mining from Term Definitions

Autor:	Liang, Zhicheng, McGuinness, Deborah L.
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Commonsense knowledge has proven to be beneficial to a variety of application areas, including question answering and natural language understanding. Previous work explored collecting commonsense knowledge triples automatically from text to increase the coverage of current commonsense knowledge graphs. We investigate a few machine learning approaches to mining commonsense knowledge triples using dictionary term definitions as inputs and provide some initial evaluation of the results. We start from extracting candidate triples using part-of-speech tag patterns from text, and then compare the performance of three existing models for triple scoring. Our experiments show that term definitions contain some valid and novel commonsense knowledge triples for some semantic relations, and also indicate some challenges with using existing triple scoring models. Comment: In the Commonsense Knowledge Graphs (CSKGs) Workshop of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2102.00651 Zobrazit plný text záznamu View this record from Arxiv