ADANS: An agriculture domain question answering system using ontologies

Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD‐5 benchmark and exhibits promising results.

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“…Some QA systems are limited to a specific domain, such as medicine, agriculture, or geography . In such systems, there is less ambiguity.…”

Section: Related Workmentioning

confidence: 99%

Ontology‐lexicon–based question answering over linked data

2020

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“…The research calculated Web page credibility score using scores from each credibility category. Since all categories are considered equal, thus the average score of the seven categories are considered as the aggregate Web page credibility score, as shown in (5).…”

Section: Web Page Credibility Scorementioning

confidence: 99%

“…QA systems are seeing a revival, primarily due to the popularity of Web, which is one of the largest repositories of data, making it the primary source of information for users and systems [5]. To find relevant information on the Web, users and systems make use of search engines [6].…”

Section: Introductionmentioning

confidence: 99%

Web Pages Credibility Scores for Improving Accuracy of Answers in Web-Based Question Answering Systems

et al. 2020

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Web-based question answering (QA) systems are effective in corroborating answers from multiple Web sources. However, Web also contains false, fabricated, and biased information that can have adverse effects on the accuracy of answers in Web-based QA systems. Existing, solutions focus primarily on finding relevant Web pages but either do not evaluate Web pages' credibility or evaluate two to three out of seven credibility categories. This research proposed a credibility assessment algorithm that uses seven categories, including correctness, authority, currency, professionalism, popularity, impartiality, quality, for scoring credibility, where each credibility category consists of multiple factors. The credibility assessment module is added on top of an existing QA system to score answers based on the credibility of Web pages. The system ranks answers based on the Web pages' credibility from where answers have been taken. The research conducted extensive quantitative tests on 211 factoid questions, taken from TREC QA data from 1999-2001. Our research findings show that credibility categories including correctness, professionalism, impartiality, and quality significantly improved the accuracy of answers. On the other hand, categories such as authority, currency, popularity played a minor role instead. This research hopes to allow researchers and experts in using the Web credibility assessment model to improve the accuracy of information systems. Credibility scores should assist Web users in selecting credible information, while also forcing content creators to focus more on publishing credible content.

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“…Wael Ghonim designed PARLIO to interact with intellects and share their opinions. Looking at agriculture as a specific domain ADANS [2] (Agriculture Domain for question answering system) uses NLP and semantic web technologies to provide answers for asked questions. The task is done in steps where preprocessing of the natural language takes place.…”

Section: Literature Surveymentioning

confidence: 99%

“…In the next step triples are formed. Ontology is built with protégé tool [2]. SPARQL is a language used to query the system to provide answers to the asked questions.…”

Section: Literature Surveymentioning

confidence: 99%

Development of Agriculture Chatbot using Machine Learning Techniques

2019

IJITEE

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Agriculture data is a main source of country’s economic growth. It is important to provide agriculture related information to all the people who are involved in agriculture activities as and when required. This meaningful information is used by people who supply services to agriculture domain and to take some correct decision related to agriculture to apply for their field. The solutions to this problem are given by the efficient interaction of computer with human. Chatbot system provides ability to extract the exact answer to the queries posed by farmers. The proposed system is called as Agriculture Chatbot system or even it is called as Question-Answering system for agriculture domain, where farmer is asking the agriculture related question which fetches the precise answers for the asked questions by farmers in natural language and processes the query using RNN (Recurrent Neural Network) deep learning algorithm to extract correct answer.

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ADANS: An agriculture domain question answering system using ontologies

Cited by 32 publications

References 3 publications

Ontology‐lexicon–based question answering over linked data

Ontology‐lexicon–based question answering over linked data

Web Pages Credibility Scores for Improving Accuracy of Answers in Web-Based Question Answering Systems

Development of Agriculture Chatbot using Machine Learning Techniques

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