Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on malay text corpus

Rodzman, Shaiful Bakhtiar bin; Ismail, Normaly Kamal; Rahman, Nurazzah Abd; Aljunid, S. A.; Nor, Zulhilmi Mohamed; Noor, Ahmad Yunus Mohd

doi:10.11591/ijeecs.v15.i3.pp1527-1534

Cited by 6 publications

(11 citation statements)

References 11 publications

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“…However, the results can be biased according to the literature review, by the variety of aspects such as degree of the previous knowledge of the sample of students according to Yilun Shang in [21], skill, preferences and also the type of genders [22], and also the small amount of sample size, and it can be the central focus for the future works of researcher to cater on this bias issues. For the future works also, the researchers would like to apply the usage of other techniques such as content-based filtering [23] and Fuzzy Logic to strengthen the analysis and decision of the current system [24] and subsequently improve the application of e-Learning in Malaysia [25].…”

Section: Results and Analysismentioning

confidence: 99%

I-OnAR: a rule-based machine learning approach for intelligent assessment in an online learning environment

Rodzman

Bakar

Choo

et al. 2020

IJEECS

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Intelligent systems are created to automate decision making process that is similar to human intelligence. Incorporating intelligent component has achieved promising results in many applications, including in education. Intelligence modules in a tutoring system would bring the application and its capability closer to a human's ability to serve its human users and to solve problems. However, the majority of the online learning provided in the literature review especially in Malaysia, normally only provide the lecture notes, assignments and tests and rarely suggest or give feedbacks on what the students should study or do next in order to fully understand the subjects. Hence, the researchers propose an online learning environment called Intelligent Online Assessment and Revision (I-OnAR). It facilitates the learning process at multiple learning phases such as test creation, materials revision, feedback for improvement and performance analysis. These components are incorporated into the tutoring system to assist self-pace learning at anytime and anywhere. The intelligent agent uses a Rule-based Machine Learning method for the adaptive capabilities such as automated test creation and feedbacks for improvement. The system has been tested on a group of students and found to be useful to support learning process. The results have shown that 60% of the subjects’ performance have improved with the help of the system. The students were given feedbacks on the topic they did poorly as well as how to improve their performance. This proves that the Intelligent Online Assessment and revision (I-OnAR) can be a useful tool to help online students intelligently, systematically and efficiently. For the future works, the researchers would like to apply the usage of other techniques such as Fuzzy Logic to strengthen the analysis and decision of the current system.

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Section: Results and Analysismentioning

confidence: 99%

I-OnAR: a rule-based machine learning approach for intelligent assessment in an online learning environment

Rodzman

Bakar

Choo

et al. 2020

IJEECS

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“…The exploration of this issue has becoming vital, since the expert judgment may have the potential to be utilized in bringing improvement of the existing ranking function of Information Retrieval (IR). Especially, in the context of Malay Translated Hadith Information Retrieval as one example of Domain Specific Text Retrieval, they possess expertise in determining the relevance of hadith in specific topics and queries and moreover assessing the authenticity of hadith documents [2]. In this study, the researchers hypothesize that the crowd judges may be have the reliability to give the relevant judgment for the certain documents and queries, but it depend on their level of confidences and also the expertise.…”

Section: Introductionmentioning

confidence: 95%

“…The system is designed based on the integration of the researchers' two previous works which are, Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on Malay text corpus in [2], and Positive Rate of Hadith in [21], with the new additional Expert Judgment with Z-Numbers that will be the main contribution in this article. The details of the design of this research will be explained as follows.…”

Section: The Systemmentioning

confidence: 99%

“…The system will use the existing method of ranking function from the researchers' previous works in [2], such as Ontology Score of BM25, that acts as the first input of ranking score in Hierarchical Fuzzy Logic System, that explain in Section b. The formula of the calculation of the Ontology Score of BM25, D will be as:…”

Section: Model For Modification a Ranking Functionmentioning

confidence: 99%

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Expert Judgment Z-Numbers as a Ranking Indicator for Hierarchical Fuzzy Logic System

Rodzman

Ismail

Rahman

et al. 2019

IJ-AI

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<p>In this article, the researchers main contribution is to investigate three factors which may correlate in implementation of Expert Judgment Z-Numbers as new Fuzzy Logic Ranking Indicator such as: expert relevance judgment or score, the expert confidence and the level of expertise. The Expert Judgment Z-Numbers then will be an input to the Hierarchical Fuzzy Logic System of Domain Specific Text Retrieval, along with other indicators such as Ontology BM25 Score, Fabrication Rate, Shia Rate and Positive Rate of hadith document. The results showed, the proposed system, with the additional new indicator of Expert Judgment Z-Numbers, may improve the original BM25 ranking function, by yielding better results on 26 queries, on all evaluation metrics that are measured in this research such as P@10, %no measures and MAP, and has achieved better results in 28 queries on P@10 alone, compared to the BM25 original score, that only yield better results in 2 queries on all evaluation metrics, and also yield better results in 4 queries on the MAP alone. The results proved that the proposed system has a capability to utilize the expert confidence and their relevant judgment that are represented in Z-Number, as an indicator to optimize the existing ranking function system and has a potential for a further research to be conducted on these domains. For the future works, the researchers would like to enhance this research by including a variety of expert’s level confidence and their judgment, also a new calculation to represent the value of Z-Numbers.</p>

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“…This structured knowledge can be understood and processed by computers to be applied in various fields [35]. Therefore, there is a lot of research in the field of ontology development to enable knowledge to be shared and reused [36]. This is no exception in the field of information retrieval to find a more accurate document representation method to produce a more effective information retrieval system.…”

Section: Test Collection (Corpus)mentioning

confidence: 99%

Open Text Ontology Mining to Improve Retrievals of Information

Hamzah¹,

Kamaruddin²

2021

IJACSA

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Information retrieval is the main task to extract relevant information from documents. Mostly, the information retrieval system is based on the keyword approach to extract the knowledge of relevant documents. The experiment shows the ontology can improve the result to overcome the weakness of keyword approach. Ontology implementation method is based on phrase formation and semantic relationships between words. This study tested 10 Malay documents using ontology to retrieve information. The results obtained were compared with the result obtained from manual information retrieval done by experts for precision and recall measure. In this study, there are three semantic relationships between words that are capable of expressing knowledge in documents. They are taxonomy relationship, attribute relationship and non-taxonomy relationship. The relationship of ontology can be formed by using taxonomy relationships algorithm, attribute relationships algorithm and non-taxonomy relationships algorithm based on the linguistic rules of the Malay language. The result of precision and recall for this experiment shows that the ontology approach can enhance the performance of information retrieval from the relevant documents.

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Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on malay text corpus

Cited by 6 publications

References 11 publications

I-OnAR: a rule-based machine learning approach for intelligent assessment in an online learning environment

I-OnAR: a rule-based machine learning approach for intelligent assessment in an online learning environment

Expert Judgment Z-Numbers as a Ranking Indicator for Hierarchical Fuzzy Logic System

Open Text Ontology Mining to Improve Retrievals of Information

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