###### Abstract  
Most educators agree that assays are the best way to evaluate students' understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback. automated essay evaluation; neutrosophic ontology; semantic analysis; knowledge representation 2023  
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Aticle An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems  
[PERSON], [PERSON], [PERSON], A. Essays are graded and evaluated by computers in a procedure known as automatic essay evaluation (AEE). Essay assessment is not the same thing as essay marking. Comparatively, the essay assessment provides students with both a grade and detailed comments on how to improve for next time. AEE is a valuable resource for educators because it not only facilitates the evaluation of student progress but also reduces costs and workload without compromising instruction quality. Numerous text mining applications can profit from using an AEE system to rate and highlight the text's substance, including cover letters, scholarly papers, email classification, and so on. An example of such applications is in the field of text mining in healthcare, where AEE can be used to rate the prescription that the doctor wrote to treat a specific disease according to the reference approved in this case for writing the prescriptions. After that, a text mining algorithm can be used to examine large collections of doctors' perceptions to discover new information or help answer specific questions. To date, most attempts at automating essay scoring (AES) have centered on identifying a suitable machine learning (ML) algorithm and identifying relevant features for teaching the ML algorithm for AES [34]. The process of grading essays is depicted in Figure 1.  
The current AES systems regularly encounter difficulties. To begin, evaluating essays is challenging because of linguistic ambiguities and the fact that there is no single \"correct\" response to any given query. To continue, e-learning material items and e-learning platforms have distinct connection infrastructures. Third, an effective essay evaluation system must gather data about the student's knowledge.