Study of Ontology-Based Intelligent Question Answering Model for Online Learning

Zhang, Zhengwan; Liu, Geping

doi:10.1109/icise.2009.1103

Cited by 3 publications

(5 citation statements)

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“…Its application can resolve the ambiguities of natural language and improve the accuracy and expressiveness of queries. In the context of quality control systems, ontologies play an important role in improving accuracy and efficiency [11]. Moreover, ontologies clearly define and describe domain knowledge in a form that is automatically readable by machines.…”

Section: Development Of An Ntological Nowledge Ase For a Uestion-nswe...mentioning

confidence: 99%

“…Moreover, ontologies clearly define and describe domain knowledge in a form that is automatically readable by machines. This makes them useful for integration into machine learning, speeding up evaluation, helping in the reflexive annotation of network resources and ensuring the execution of logical work [11]. In [12], a new process for analyzing issues using ontologies is proposed.…”

Section: Development Of An Ntological Nowledge Ase For a Uestion-nswe...mentioning

confidence: 99%

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Development of an Ontological Knowledge Base for a Question–Answering System in the Kazakh Language

2023

International Conference Proceedings IICSET-23, ACBES-23 &Amp; LMSSH-23 Dec. 14-15, 2023 Istanbul (Turkiye)

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This article describes a knowledge base based on an ontological model for a question-answering system in the Kazakh language. With the explosive growth of information and the constant accumulation of redundant information, it has become much more difficult to accurately and quickly obtain valuable information. The variety and volume of data available on the web require effective tools to extract, process and provide information to users. Therefore, the creation of an intelligent question-answering system in the Kazakh language based on the presentation, processing and extraction of knowledge can provide a significant contribution to improving access to information for Kazakh-speaking users. It also contributes to the preservation and promotion of the Kazakh language, expanding its presence in the digital space.

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Section: Development Of An Ntological Nowledge Ase For a Uestion-nswe...mentioning

confidence: 99%

Section: Development Of An Ntological Nowledge Ase For a Uestion-nswe...mentioning

confidence: 99%

Development of an Ontological Knowledge Base for a Question–Answering System in the Kazakh Language

2023

International Conference Proceedings IICSET-23, ACBES-23 &Amp; LMSSH-23 Dec. 14-15, 2023 Istanbul (Turkiye)

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“…It can be employed to resolve the obscurity of NLQs, and also to develop the accuracy and expressiveness of queries. Ontology has a significant role to play in developing the accuracy and effectiveness of QA systems [43]. Moreover, ontologies characterize and describe domain knowledge clearly in an auto-readable structure, and thus can be incorporated into machine-based formations designed to accelerate assessment, aid the reflexive annotation of net resources, and perform logical work [44].…”

Section: Ontologymentioning

confidence: 99%

“…However, it can be argued that precision would be a more effective measure. To balance the trade-off, [14,43] introduced the 'f' calculation (Formula (4)).…”

Section: Qa System Evaluationmentioning

confidence: 99%

“…This metric applied a weighted score, gauging precision and recall trade-off. According to [14,43,85], the following metrics (Formulas (5) and ( 6)) can be used to calculate QA systems, and include calculations such as the mean average precision (MAP), which is a universal assessment for IR, describing the (AveP), average precision, given by Formula (5). Meanwhile, the mean reciprocal rank (MRR) (Formula ( 7)) can be used to calculate relevance.…”

Section: Qa System Evaluationmentioning

confidence: 99%

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Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review

Arbaaeen

Shah

2021

Information

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For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain.

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Automatic Question-Answering Based on Wikipedia Data Extraction

Huang

Wei

Zhang

2015

2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

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Study of Ontology-Based Intelligent Question Answering Model for Online Learning

Cited by 3 publications

References 3 publications

Development of an Ontological Knowledge Base for a Question–Answering System in the Kazakh Language

Development of an Ontological Knowledge Base for a Question–Answering System in the Kazakh Language

Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review

Automatic Question-Answering Based on Wikipedia Data Extraction

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