Question answering with a conceptual framework for knowledge-based system development “Node of Knowledge”

Pavlić, Mile; Han, Zdravko Dovedan; Jakupović, Alen

doi:10.1016/j.eswa.2015.02.024

Cited by 23 publications

(7 citation statements)

References 40 publications

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“…Question Answering systems presenting an interface, where users could state their demand for information in the Natural Language format and the search engine will produce suitable answers to these questions [22][23][24]. More specific described by [25], question answering system is a technology used to find, extract, and provide a proper answer to the user's query in the natural language format. This system consists of several essential components, i.e.…”

Section: Question Answering Systemmentioning

confidence: 99%

New instances classification framework on Quran ontology applied to question answering system

2019

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Instances classification with the small dataset for Quran ontology is the current research problem which appears in Quran ontology development. The existing classification approach used machine learning: Backpropagation Neural Network. However, this method has a drawback; if the training set amount is small, then the classifier accuracy could decline. Unfortunately, Holy Quran has a small corpus. Based on this problem, our study aims to formulate new instances classification framework for small training corpus applied to semantic question answering system. As a result, the instances classification framework consists of several essential components: pre-processing, morphology analysis, semantic analysis, feature extraction, instances classification with Radial Basis Function Networks algorithm, and the transformation module. This algorithm is chosen since it robustness to noisy data and has an excellent achievement to handle small dataset. Furthermore, document processing module on question answering system is used to access instances classification result in Quran ontology.

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Section: Question Answering Systemmentioning

confidence: 99%

New instances classification framework on Quran ontology applied to question answering system

2019

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“…Experimental results show that using information from the external corpora, framework produces imperative improvements on question pattern identification, dynamic document clustering based on domain context, especially on datasets with short documents [19]. The result analysis for the query input ''what is da vinci code? ''…”

Section: System Evaluationmentioning

confidence: 99%

A framework for intelligent question answering system using semantic context-specific document clustering and Wordnet

Karpagam¹,

Saradha²

2019

Sādhanā

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The question answering system plays an important role in information retrieval field, where the user is in need of getting a precise answer instead of large collections of documents. The aim of this paper is to investigate techniques for improving sentence-based question answering system. To achieve this, a POS-Taggerbased question pattern analysis model is proposed to identify question type based on pattern template for the user-submitted query. Next, the knowledge base is created from a large corpus by clustering the documents by grouping on domain context. The proposed semantic-word-based answer generator model deals with the user query mapping with an appropriate sentence in the knowledge base. By the proposed models, the system reduces the search gap among user queries and answer sentences using Wordnet. It considers word order, overlap, sentence similarity, string distance, unambiguous words and semantic similarity of words. The proposed algorithm evaluates with benchmark datasets such as 20Newsgroup and TREC-9 QA, and proves its efficiency by statistical test for significance.

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“…It requires correct grammatical sentences to perform well. Pavlić, Dovedan Han, and Jakupović (2015) developed a conceptual framework to represent text expressed knowledge. They had to un-contextualize the text for processing, and that is the limitation of this work.…”

Section: Related Workmentioning

confidence: 99%

Focused domain contextual AI chatbot framework for resource poor languages

Paul

Latif

Adnan

et al. 2018

Journal of Information and Telecommunication

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In today's business world, providing reliable customer service is equally important as delivering better products for maintaining a sustainable business model. As providing customer service requires human resource and money, businesses are often shifting towards artificial intelligence system for necessary customer interaction. However, these traditional chatbot architectures depend heavily on natural language processing (NLP), it is not feasible to implement for the languages with little to no prior NLP backbone. In this work, we propose a semi-supervised artificially intelligent chatbot framework that can automate parts of primary interaction and customer service. The primary focus of this work is to build a chatbot which can generate contextualized responses in any language without depending much on rich NLP background and a vast number of a prior data set. This system is designed in such a way that with a dictionary of a language and regular customer interaction dataset, it can provide customer services for any business in any language. This architecture has been used to build a customer service bot for an electric shop, and different analysis has been done to evaluate the performance of individual components of the framework to show its competence to provide reliable response generation in comparison with other approaches. ARTICLE HISTORY

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Question answering with a conceptual framework for knowledge-based system development “Node of Knowledge”

Cited by 23 publications

References 40 publications

New instances classification framework on Quran ontology applied to question answering system

New instances classification framework on Quran ontology applied to question answering system

A framework for intelligent question answering system using semantic context-specific document clustering and Wordnet

Focused domain contextual AI chatbot framework for resource poor languages

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