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

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Data Management, Analytics and Innovation

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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Acknowledgements

This work was supported by the Maharashtra Government Project funded by the Rajiv Gandhi Science & Technology Commission Mumbai. We also thank to Marathi Vidnyan Parishad, Pune Vibhag for their immense help in writing Marathi definitions.

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Correspondence to Neelam Chandolikar .

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Chandolikar, N., Joglekar, P., Bhosale, S., Peddawad, D., Jalnekar, R., Shilaskar, S. (2020). Semiautomated Ontology Learning to Provide Domain-Specific Knowledge Search in Marathi Language. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-32-9949-8_33

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

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