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.
%0 Journal Article
%1 Dasgupta2018-xc
%A Dasgupta, Sourish
%A Padia, Ankur
%A Maheshwari, Gaurav
%A Trivedi, Priyansh
%A Lehmann, Jens
%D 2018
%I arXiv
%K
%T Formal ontology learning from English IS-A sentences
%X 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.
@article{Dasgupta2018-xc,
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.},
added-at = {2024-09-10T11:56:37.000+0200},
author = {Dasgupta, Sourish and Padia, Ankur and Maheshwari, Gaurav and Trivedi, Priyansh and Lehmann, Jens},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/27861d1fde0ba131b6ba72786e0b404f9/scadsfct},
interhash = {52d07390e263bf21545db877418ccf6b},
intrahash = {7861d1fde0ba131b6ba72786e0b404f9},
keywords = {},
publisher = {arXiv},
timestamp = {2024-09-10T15:15:57.000+0200},
title = {Formal ontology learning from English {IS-A} sentences},
year = 2018
}
