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      Formal Ontology Learning on Factual IS-A Corpus in English using Description Logics

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          Abstract

          Ontology Learning (OL) is the computational task of generating a knowledge base in the form of an ontology given an unstructured corpus whose content is in natural language (NL). Several works can be found in this area most of which are limited to statistical and lexico-syntactic pattern matching based techniques Light-Weight OL. These techniques do not lead to very accurate learning mostly because of several linguistic nuances in NL. Formal OL is an alternative (less explored) methodology were deep linguistics analysis is made using theory and tools found in computational linguistics to generate formal axioms and definitions instead simply inducing a taxonomy. In this paper we propose "Description Logic (DL)" based formal OL framework for learning factual IS-A type sentences in English. We claim that semantic construction of IS-A sentences is non trivial. Hence, we also claim that such sentences requires special studies in the context of OL before any truly formal OL can be proposed. We introduce a learner tool, called DLOL_IS-A, that generated such ontologies in the owl format. We have adopted "Gold Standard" based OL evaluation on IS-A rich WCL v.1.1 dataset and our own Community representative IS-A dataset. We observed significant improvement of DLOL_IS-A when compared to the light-weight OL tool Text2Onto and formal OL tool FRED.

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          Author and article information

          Journal
          2013-12-25
          2016-03-08
          Article
          1312.6947
          ad275f4d-c913-4f53-828d-c14b041fb3c2

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          This paper has been withdrawn by the author due to requirement of re-evaluation of results
          cs.CL cs.AI

          Theoretical computer science,Artificial intelligence
          Theoretical computer science, Artificial intelligence

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