text: 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.
  @cite_35: Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.
summary: There has been significant literature over the last decade on the problem of Ontology Learning (OL). Most of these works can be categorized into two approaches as discussed earlier: (i) , and (ii) . Light-weight OL from text documents is arguably the most widely used approach in the field of OL @cite_35 . It can be further divided into three general approaches: (i) , (ii) , and (iii) . It is to be noted that the general disadvantage of light-weight OL of not being able to generate definitional T-Box (and corresponding A-Box), as discussed in the introduction section, is inherent in all the three approaches. This is where @math parts away significantly from light-weight OL approaches. We first discuss light-weight OL in the following three sub-sections for providing a contrasting perspective, and then conclude the section with a discussion on formal OL.