DLOLIS-A: Description Logic based Text Ontology Learning
  Ontology Learning has been the subject of intensive study for the past
decade. Researchers in this field have been motivated by the possibility of
automatically building a knowledge base on top of text documents so as to
support reasoning based knowledge extraction. While most works in this field
have been primarily statistical (known as light-weight Ontology Learning) not
much attempt has been made in axiomatic Ontology Learning (called heavy-weight
Ontology Learning) from Natural Language text documents. Heavy-weight Ontology
Learning supports more precise formal logic-based reasoning when compared to
statistical ontology learning. In this paper we have proposed a sound Ontology
Learning tool DLOL_(IS-A) that maps English language IS-A sentences into their
equivalent Description Logic (DL) expressions in order to automatically
generate a consistent pair of T-box and A-box thereby forming both regular
(definitional form) and generalized (axiomatic form) DL ontology. The current
scope of the paper is strictly limited to IS-A sentences that exclude the
possible structures of: (i) implicative IS-A sentences, and (ii) "Wh" IS-A
questions. Other linguistic nuances that arise out of pragmatics and epistemic
of IS-A sentences are beyond the scope of this present work. We have adopted
Gold Standard based Ontology Learning evaluation on chosen IS-A rich Wikipedia
documents.
