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2020 | OriginalPaper | Chapter

Semiautomated Ontology Learning to Provide Domain-Specific Knowledge Search in Marathi Language

Authors : Neelam Chandolikar, Pushkar Joglekar, Shivjeet Bhosale, Dipali Peddawad, Rajesh Jalnekar, Swati Shilaskar

Published in: Data Management, Analytics and Innovation

Publisher: Springer Singapore

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Abstract

In this research work, our goal is to build a self-sustainable, reproducible, and extensive domain-specific ontology for the purposes of creating a knowledge search engine. We have used online data as the primary information store using which we construct ontology by identifying concepts (nodes) and relationships between concepts. The project encompasses preestablished ideas gathered from successful NLP trials and presents a new variation to the task of ontology creation. The system, for which the ontology is being created, is a knowledge search engine in Marathi. This aims at building semiautomated ontology whose target demographic is primary school children and the selected domain is science domain. This project proposes a method to build semiautomated ontology. We use a combination of natural language processing method and machine learning method to automate the ontology learning task. Automatically learned ontology is further modified by language and domain experts to enrich the contents of ontology. Unlike, standard search engines, our knowledge search engine attempts to provide learned resources directly to the user rather than website links. This approach enables the user to directly get information without having to spend time on browsing indexed links.

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Metadata
Title
Semiautomated Ontology Learning to Provide Domain-Specific Knowledge Search in Marathi Language
Authors
Neelam Chandolikar
Pushkar Joglekar
Shivjeet Bhosale
Dipali Peddawad
Rajesh Jalnekar
Swati Shilaskar
Copyright Year
2020
Publisher
Springer Singapore
Book
Data Management, Analytics and Innovation

Print ISBN: 978-981-329-948-1

Electronic ISBN: 978-981-329-949-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-32-9949-8_33