Formal Ontology Learning from English IS-A Sentences
Abstract
Ontology learning (OL) is the process of automatically generating an ontological knowledge base from a plain text document. In this paper, we propose a new ontology learning approach and tool, called DLOL, which generates a knowledge base in the description logic (DL) SHOQ(D) from a collection of factual non-negative IS-A sentences in English. We provide extensive experimental results on the accuracy of DLOL, giving experimental comparisons to three state-of-the-art existing OL tools, namely Text2Onto, FRED, and LExO. Here, we use the standard OL accuracy measure, called lexical accuracy, and a novel OL accuracy measure, called instance-based inference model. In our experimental results, DLOL turns out to be about 21% and 46%, respectively, better than the best of the other three approaches.
Keywords
Cite
@article{arxiv.1802.03701,
title = {Formal Ontology Learning from English IS-A Sentences},
author = {Sourish Dasgupta and Ankur Padia and Gaurav Maheshwari and Priyansh Trivedi and Jens Lehmann},
journal= {arXiv preprint arXiv:1802.03701},
year = {2018}
}