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

Utomo, Fandy Setyo; Suryana, Nanna; Azmi, Mohd Sanusi

doi:10.12928/telkomnika.v17i1.9794

Cited by 11 publications

(15 citation statements)

References 34 publications

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“…The convolution window size of each layer is 5x100, 4x1, 2x1 and the pooling window size is 4x100, 3x1, 3x1. This configuration is also denoted as (5,4,2) and (4,3,3). The RNN is modelled with 2 LSTM units and the size of all LSTM units is set to 360.…”

Section: Resultsmentioning

confidence: 99%

“…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. When compared with the traditional information retrieval system, the relevant amount of information is considered as an answer instead of retrieving the entire document [2]. Data mining is a subfield of computer science that enables intelligent extraction of useful information [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Convolutional recurrent neural network with template based representation for complex question answering

Reddy¹,

Madhavi²

2020

IJECE

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Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convolutional recurrent neural network with template based representation for complex question answering

Reddy¹,

Madhavi²

2020

IJECE

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“…In addition, to further improve data and information management, one would create an ontologybased application that processes all knowledge related to a given topic. Such a process would involve knowledge modeling through an ontology [14].…”

Section: Ontologymentioning

confidence: 99%

An ontology driven question answering system for fatawa retrieval

Benlaharche

Laboudi

Nouaouria

et al. 2021

IJEECS

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This work aims to propose a system for the Algerian Fatawa House in orderto facilitate the task of the Expert Mufti who is responsible of giving fatawa for Algerian people inquiries. In fact, as this house is recent and does not have sufficient human resources, it is difficult to satisfy all inquiries coming daily, this leads the askers to wait for a long time before getting answers. The proposed system allows the askers to express concerns they may have. By using a case-based reasoning mechanism combined with ontology domain, the system tries to retrieve similar cases from the knowledge base. In the casewhere the response already exists, the system immediately provides the answer to the askers. Otherwise, an inquery is automatically formulated and sent to the expert Mufti-which is a certified scholar-in order either to validate the generated response by the system or give a new answer. Such a question-answering system may be very helpful for askers to get their answers faster since it allows both the storage of previous <em>fatawas</em> and their retrieval for processing coming inquiries. To validate our proposal, we rely on <em>fatawas</em> concerning the Islamic finance and banking transactions domain. Overall, the results are encouraging and satisfactory.

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“…The knowledge inside the Holy Quran n could be stored and represented by ontology. There are two approaches to build an ontology, i.e., non-automated and automated process [1]. This automatic process is also known as the ontology population.…”

Section: Introductionmentioning

confidence: 99%

Stemming Impact Analysis on Indonesian Quran Translation and Their Tafsir Classification for Ontology Instances

Utomo

Suryana

Azmi

2020

IIUMEJ

Self Cite

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The current gap which appears in the Quran ontology population domain is stemming impact analysis on Indonesian Quran translation and their Tafsir to develop ontology instances. The existing studies of stemming effect analysis performed in various languages, dataset, stemming method, cases, and classifier. However, there is a lack of literature that studies about stemming influence on instances classification for Quran ontology with different dataset, classifier, Quran translation, and their Tafsir on Indonesian. Based on this problem, our study aims to investigate and analyze the stemming impact on instances classification results using Indonesian Quran translation and their Tafsir as datasets with multiple supervised classifiers. Our classification framework consists of text pre-processing, feature extraction, and text classification stage. Sastrawi stemmer was used to perform stemming operation in text pre-processing stage. Based on our experiment results, it was found that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation owns the best classification performance, i.e., 70.75% for accuracy and 71.55% for precision in Indonesian Quran translation dataset on 20% test data size. While in 30% test data size, SVM and TF-IDF with stemming process own the best classification performance, i.e., 67.30% for accuracy and 68.10% for precision in Ministry of Religious Affairs Indonesia dataset. Furthermore, in this study, it was also discovered that the Backpropagation Neural Network has the most precision and accuracy reduction due to the negative impact of stemming operations.

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

Cited by 11 publications

References 34 publications

Convolutional recurrent neural network with template based representation for complex question answering

Convolutional recurrent neural network with template based representation for complex question answering

An ontology driven question answering system for fatawa retrieval

Stemming Impact Analysis on Indonesian Quran Translation and Their Tafsir Classification for Ontology Instances

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