Volume 40, Issue 10 e13434
ORIGINAL ARTICLE

PooRaa-Agri KG: An agricultural knowledge graph-based simplified multilingual query system

Nethraa Sivakumar

Corresponding Author

Nethraa Sivakumar

Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India

Correspondence

Nethraa Sivakumar, Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India.

Email: [email protected]

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Pooja Srinivasan

Pooja Srinivasan

Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India

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Mrinalini Kannan

Mrinalini Kannan

Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India

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Vijayalakshmi P

Vijayalakshmi P

Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India

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Nagarajan T

Nagarajan T

Department of Computer Science Engineering, Shiv Nadar University, Chennai, Tamil Nadu, India

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First published: 24 August 2023

Abstract

The current work proposes PooRaa-Agri KG, an agricultural knowledge graph-based simplified multilingual query system that works in real time to provide concise answers for agriculture-based queries. The proposed approach accommodates real-time and low-resource queries in English and Hindi with a novel multi-stage solution consisting of data pre-processing, sentence simplification, triplet extraction, knowledge graph generation, sentence reconstruction, query-to-reconstructed sentence matching, and machine translation as its sub-modules. In this work, a novel combination of rule-based sentence simplification and triplet extraction is carried out resulting in a triplet similarity score of 86.56% for the extracted triplets. This method is superior to the existing triplet extraction method whose triplet similarity score was found to be 60.65%. Further, the proposed work makes use of heuristic rules to reconstruct sentences which when evaluated by human evaluators for meaningfulness and grammar resulted in a score of 3.09/4 and 2.95/4 respectively. To complete end-to-end communication in the proposed system, a similarity-based query answer system is proposed in this work.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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