Formal Ontology Learning from English IS-A Sentences
This work addresses the need for efficient ontology learning in knowledge representation, offering a tool with significant accuracy gains over existing methods.
The paper tackles the problem of automatically generating formal ontologies from English IS-A sentences, proposing DLOL, which achieves 21% and 46% improvements over state-of-the-art tools in lexical and instance-based accuracy measures.
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.