AOQAS: Ontology Based Question Answering System for Agricultural Domain

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A8995PN5X" target="_blank" >RIV/00216208:11320/25:8995PN5X - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200978597&partnerID=40&md5=f2f6d3a2c788c5a2c7cdd28da116a82c" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200978597&partnerID=40&md5=f2f6d3a2c788c5a2c7cdd28da116a82c</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

AOQAS: Ontology Based Question Answering System for Agricultural Domain

Popis výsledku v původním jazyce

Agriculture is an indispensable sector for human community that has been transformed by technological innovations. The data handling with information extraction is one of the areas that is benefited by the advancements in information technology. The presented research work aims to develop a question answering system (QAS) for improving the information retrieval from the agricultural text documents. The proposed Agriculture domain Ontology based QAS (AOQAS) processes the given agricultural text documents and constructs it to a knowledge representation called ontology. The domain based ontology is created using the Bidirectional Encoder Representations from Transformers model (BERT model) with Regular Expressions (RE) for withdrawing domain terms and the Bidirectional Long Short Term Memory model (BiLSTM) with RE for relationship extraction between the agricultural terms. From the developed ontology, the answers for the input query are extracted and validated using Natural Language Processing (NLP) techniques and the deep learning model. The proposed AOQAS shows an accuracy and recall of 98.47% and 98.26%. The outcomes of AOQAS shows better performance when it is evaluated against the current systems. © Cerebration Science Publishing.

Název v anglickém jazyce

AOQAS: Ontology Based Question Answering System for Agricultural Domain

Popis výsledku anglicky

Agriculture is an indispensable sector for human community that has been transformed by technological innovations. The data handling with information extraction is one of the areas that is benefited by the advancements in information technology. The presented research work aims to develop a question answering system (QAS) for improving the information retrieval from the agricultural text documents. The proposed Agriculture domain Ontology based QAS (AOQAS) processes the given agricultural text documents and constructs it to a knowledge representation called ontology. The domain based ontology is created using the Bidirectional Encoder Representations from Transformers model (BERT model) with Regular Expressions (RE) for withdrawing domain terms and the Bidirectional Long Short Term Memory model (BiLSTM) with RE for relationship extraction between the agricultural terms. From the developed ontology, the answers for the input query are extracted and validated using Natural Language Processing (NLP) techniques and the deep learning model. The proposed AOQAS shows an accuracy and recall of 98.47% and 98.26%. The outcomes of AOQAS shows better performance when it is evaluated against the current systems. © Cerebration Science Publishing.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Computer Information Systems and Industrial Management Applications

ISSN

2150-7988

e-ISSN

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Svazek periodika

16

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

230-245

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-85200978597