Formal Ontology Learning from English IS-A Sentences

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 fa...

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Bibliographic Details
Published inarXiv.org
Main Authors Dasgupta, Sourish, Padia, Ankur, Maheshwari, Gaurav, Trivedi, Priyansh, Lehmann, Jens
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 11.02.2018
Subjects
Accuracy
Knowledge base
Learning
Model accuracy
Ontology
Sentences
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Summary: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.
ISSN:2331-8422