Research and implementation of automatic question answering system based on ontology

Xie, Xingbo; Song, Wei; Liu, Lizhen; Du, Chao; Wang, Hanshi

doi:10.1109/ccdc.2015.7162131

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…Table 4 describes the key experimental parameters and values which were used for training the Seq2Seq and transformer learning models in this study. The interested reader is referred to the TensorFlow documentation 13 for more details on these parameters.…”

Section: Methodsmentioning

confidence: 99%

“…Question-answering (QA) systems [1]- [9] provide intelligence analysts and other users of information systems with the ability to pose questions to a system in natural language (semantic queries), and obtain the relevant answers (assistance) they may require to better perform their tasks [10]- [13]. QA systems have historically been successfully utilised for information retrieval in domains like e-commerce [14], education [15], tourism [16] and safety-critical applications like healthcare [17]- [19] etc.…”

Section: Introductionmentioning

confidence: 99%

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Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

Chatterjee

Dethlefs

2022

IEEE Access

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Intelligent question-answering (QA) systems have witnessed increased interest in recent years, particularly in their ability to facilitate information access, data interpretation or decision support. The wind energy sector is one of the most promising sources of renewable energy, yet turbines regularly suffer from failures and operational inconsistencies, leading to downtimes and significant maintenance costs. Addressing these issues requires rapid interpretation of complex and dynamic data patterns under time-critical conditions. In this article, we present a novel approach that leverages interactive, natural language-based decision support for operations & maintenance (O&M) of wind turbines. The proposed interactive QA system allows engineers to pose domain-specific questions in natural language, and provides answers (in natural language) based on the automated retrieval of information on turbine subcomponents, their properties and interactions, from a bespoke domain-specific knowledge graph. As data for specific faults is often sparse, we propose the use of paraphrase generation as a way to augment the existing dataset. Our QA system leverages encoder-decoder models to generate Cypher queries to obtain domain-specific facts from the KG database in response to user-posed natural language questions. Experiments with an attention-based sequence-to-sequence (Seq2Seq) model and a transformer show that the transformer accurately predicts up to 89.75% of responses to input questions, outperforming the Seq2Seq model marginally by 0.76%, though being 9.46 times more computationally efficient. The proposed QA system can help support engineers and technicians during O&M to reduce turbine downtime and operational costs, thus improving the reliability of wind energy as a source of renewable energy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

Chatterjee

Dethlefs

2022

IEEE Access

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“…In this work, the researchers used Natural Language Processing (NLP) techniques, including NER (Named Entity Recognition), POS, and dependency parsing. Moreover, similar techniques were used by Xie et al [22], including POS tagging, syntactic analysis, and semantic relation query keywords to develop a QA system based on ontology.…”

Section: A Linguistic Approach-based Qa Modelsmentioning

confidence: 99%

AgriResponse: A Real-Time Agricultural Query-Response Generation System for Assisting Nationwide Farmers

Godara,

Bedi,

Parsad

et al. 2024

IEEE Access

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Advancements in information sciences can play a vital role in strengthening the nation's sustainable agriculture goals. In this direction, we propose a framework for a text-based query-response generation system to cope with the demand for timely help to the nationwide Indian farmers. One of the major challenges in designing such systems is constructing a knowledge base that can answer plantprotection-related questions from a diverse population of farmers. To tackle this problem, the past eight years' call-log records from the countrywide farmers' helpline network are collected and processed to construct the required knowledge base. Additionally, three response-retrieval models with approximate matching and spatial-based searching functionality are developed to administer the user input questions and extract relevant answers from the base. To validate the performance of the proposed framework, a diversified question bank consisting of 755 queries covering 151 crops in India is compiled. Three metrics (Accuracy Percentage, Crop-weighted Performance Score, and Average Response-retrieval time) are considered for the models' assessment. Experimental results show that AgriResponse is a practical framework in real-world applications, with the different retrieval models useful for different scenarios.

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“…Gupta and Gupta (2014) implemented a hybrid QA system that works on various kinds of question types using the concepts of pattern matching and mathematical expression. Xie et al (2015) built a curriculum domain knowledge-based ontology to solve existing problems of extant AQA systems, such as inadequate knowledge expression and weakness of indicating the inherent relations among knowledge.…”

Section: Literature Reviewmentioning

confidence: 99%

Online investigation of users’ attitudes using automatic question answering

Zhang

Zhou

2018

OIR

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Purpose With the development of the internet, huge numbers of reviews are generated, disseminated, and shared on e-commerce and social media websites by internet users. These reviews usually indicate users’ opinions about products or services directly, and are thus valuable for efficient marketing. The purpose of this paper is to mine online users’ attitudes from a huge pool of reviews via automatic question answering. Design/methodology/approach The authors make use of online reviews to complete an online investigation via automatic question answering (AQA). In the process of AQA, question generation and extraction of corresponding answers are conducted via sentiment computing. In order to verify the performance of AQA for online investigation, online reviews from a well-known travel website, namely Tuniu.com, are used as the experimental data set. Finally, the experimental results from AQA vs a traditional questionnaire are compared. Findings The experimental results show that results between the AQA-based automatic questionnaire and the traditional questionnaire are consistent. Hence, the AQA method is reliable in identifying users’ attitudes. Although this paper takes Chinese tourism reviews as the experimental data, the method is domain and language independent. Originality/value To the best of the authors’ knowledge, this is the first study to use the AQA method to mine users’ attitudes towards tourism services. Using online reviews may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

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Research and implementation of automatic question answering system based on ontology

Cited by 8 publications

References 2 publications

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

AgriResponse: A Real-Time Agricultural Query-Response Generation System for Assisting Nationwide Farmers

Online investigation of users’ attitudes using automatic question answering

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