Formal concept analysis (FCA) is today regarded as a significant technique for knowledge extraction, representation, and analysis for applications in a variety of fields. Significant progress has been made in recent years to extend FCA theory to deal with uncertain and imperfect data. The computational complexity associated with the enormous number of formal concepts generated has been identified as an issue in various applications. In general, the generation of a concept lattice of sufficient complexity and size is one of the most fundamental challenges in FCA. The goal of this work is to provide an overview of research articles that assess and compare numerous fuzzy formal concept analysis techniques which have been suggested, as well as to explore the key techniques for reducing concept lattice size. as well as we'll present a review of research articles on using fuzzy formal concept analysis in ontology engineering, knowledge discovery in databases and data mining, and information retrieval.

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Formal concept analysis (FCA) is now widely recognized as a useful approach for extracting, representing, and analyzing knowledge in various domains. The high computational cost of knowledge processing and the difficulty of visualizing the lattice are two key challenges in practical FCA implementations. Moreover, assessing the finalized built-up lattice may be problematic due to the enormous number of formal concepts and the complexity of their connections. The challenge of constructing concept lattices of adequate size and structure to convey high-importance context features remains a significant FCA aim. In the literature, various strategies for concept lattice reduction have been presented. In this work, we suggest a categorization of reduction methods for concept lattice based on three main categories: context pre-processing, non-essential distinctions elimination, and concept filtration, whereby using FCA-based analysis, the most important methods in the literature are analyzed and compared based on six pillars: the preliminary step of the reduction process, domain expert, changing the original data structure, final concept lattice, quality of reduction, and category of reduction method.

Artificial intelligence systems offer valuable support to the education sector by automating routine tasks for instructors and providingadaptive assessments. These assessments are a crucial component ofautomatic question-generation techniques. There are various techniquesfor question generation described in the literature. This paper introduces a novel rule-based system for automatic question generationthat employs dependency parsing techniques. The proposed methodfocuses on analyzing both the syntactic and semantic structure of asentence. The paper includes both manual and automatic evaluationmetrics to assess the questions generated by our proposed system. Inthe evaluation, our system received an overall score of 3.67 out of5.0 for human-level performance, an average BlEU−N score of 0.718,and F1−score scores above 0.5 for all types of ROUGE. Both humanand automatic evaluation results demonstrate that our proposed system achieves good performance on simple and short sentences. In thefuture, we plan to enhance our model by incorporating phrase-levelparsing to further improve the proposed dependency parsing techniques.

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