Enhancements to knowledge discovery framework of SOPHIA textual case-based reasoning

Elhalwany, Islam; Mohammed, Ammar; Wassif, Khaled; Hefny, Hesham A.

doi:10.1016/j.eij.2014.10.002

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Moreover, in another work presented by the same authors [10], the only results shown are clustering results. However, neither the software applications nor the textual resources used in the experiments are available for comparison.…”

Section: Resultsmentioning

confidence: 99%

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Answer Extraction System Based on Latent Dirichlet Allocation

Ali¹,

Abdou²

2016

ijacsa

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Abstract-Question Answering (QA) task is still an active area of research in information retrieval. A variety of methods which have been proposed in the literature during the last few decades to solve this task have achieved mixed success. However, such methods developed in the Arabic language are scarce and do not have a good performance record. This is due to the challenges of Arabic language. QA based on Frequently Asked Questions is an important branch of QA in which a question is answered based on pre-answered ones. In this paper, the aim is to build a question answering system that responds to a user inquiry based on pre-answered questions. The proposed approach is based on Latent Dirichlet Allocation. Firstly, the dataset, pairs of questions and associated answers, will be grouped into several clusters of related documents. Next, when a new question to be answered is posed to the system, it,therefore, starts to assign this question to its appropriate cluster, then, use a similarity measure to get the top ten closest possible answers. Preliminary results show that the proposed method is achieving a good level of performance.

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Section: Resultsmentioning

confidence: 99%

“…However, in [9], [10] Islam Elhelwany et.al. have proposed an Arabic Fatwa Intelligent system based on textual case based rezoning which was firstly used in [11].…”

Section: A Previous Workmentioning

confidence: 99%

Answer Extraction System Based on Latent Dirichlet Allocation

Ali¹,

Abdou²

2016

ijacsa

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“…• Enhancements to knowledge discovery framework of SOPHIA textual case-based reasoning [26] In [26] The same authors of the previous system have enhanced their previous study by adding one tier in the middle. This tier was created by manually clustering the dataset into several groups so the SOPHIA will be applied to each group separately and that what make an enhancement as they reported.…”

Section: B Qa Based On Faqmentioning

confidence: 99%

Arabic Text Question Answering from an Answer Retrieval Point of View: a survey

Sati¹,

Ali²,

Abdou³

2016

ijacsa

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Abstract-Arabic Question Answering (QA) is gaining more importance due to the importance of the language and the dramatic increase in online Arabic content. The goal of this article is to review the state-of-the-art of Arabic QA methods, to classify them into different categories from an answer retrieval viewpoint and to present their applications, issues and new trends. The main components of question answering systems are also presented. Finally, this survey provides a comparative study of systems of each type of QA based on several criteria.

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“…Textual Case-Based Reasoning (TCBR) is a CBR sub field that compares a problem with a set of past cases where the knowledge sources are available in textual format. Comparing text content is vital to identify relevant cases for solution reuse [9]. However, Textual information typically is hard to process by computers due to its relatively unstructured format and the numerous possible variations in its interpretation.…”

Section: Introductionmentioning

confidence: 99%

CBR-Based Decision Support System for Maintenance Text Using NLP for an Aviation Case Study

Naqvi

Ghufran

Meraghni

et al. 2022

2022 Prognostics and Health Management Conference (PHM-2022 London)

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Recently, Prognostics and Health Management (PHM) has emerged to promote predictive maintenance as a methodological key to overcome the limitations of traditional reliability analysis. The Natural Language Processing (NLP) methods allow the maintenance log usage for maintenance diagnostics and decision making. The Maintenance Work Orders (MWOs) contain vital health indicators and decades of experience related to various maintenance actions. However, due to the unstructured nature of maintenance text, it is not common to develop a tool using these textual maintenance entries. This paper proposes a textual Case-Based Reasoning (CBR) approach combined with Technical Language Processing (TLP) to find solutions for new problems based on previous experiences. The Bidirectional Encoder Representations from Transformers (BERT) model is adopted for maintenance data using unsupervised fine-tuning technique Transformer-based Sequential Denoising Auto-Encoder (TSDAE) for aviation case study. Results show that the pre-trained BERT model can adopt domain-specific data and produce semantic matches with only a small amount (1000 samples) of domain specific data.

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Enhancements to knowledge discovery framework of SOPHIA textual case-based reasoning

Cited by 6 publications

References 9 publications

Answer Extraction System Based on Latent Dirichlet Allocation

Answer Extraction System Based on Latent Dirichlet Allocation

Arabic Text Question Answering from an Answer Retrieval Point of View: a survey

CBR-Based Decision Support System for Maintenance Text Using NLP for an Aviation Case Study

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