###### Abstract
Information retrieval, data mining, and natural language processing systems have all benefited from word-based and statistical methodologies, but there is still an urgent need for deeper text understanding. A fourth area where improvement in textual information handling is needed is in the area of ideas, semantic connections, and the surrounding information required for the notion of disambiguation. Finally, the method needs to be trustworthy and practical for educators [5; 6; 7]. The current AES approaches take numerous perspectives into account when analyzing the essay. Some of them give the essay a grade based on its quality as described by a set of characteristics that can be roughly categorized as style, content, and semantic attributes [8; 9; 10; 11]. The vocabulary, syntax, and techniques (spelling, capitalization, and punctuation) are the main focuses of style. Most systems for evaluating writing styles rely on statistical techniques,  
Figure 1: Essay scoring system. which produce reliable outcomes but struggle to handle morphology when evaluating style attributes. An essay's content characteristics are founded on comparisons to the essay's source text and to other essays that have already been evaluated, and they provide only a hazy description of the essay's semantics. The foundation of semantic characteristics lies in ensuring that the content's meaning is accurate. Attributes from essays can be extracted using several different methods and tools. The most popular approach uses natural language processing (NLP)-based techniques. Latent semantic analysis (LSA) is widely used by content-centric systems; it is a machine learning technique for analyzing the relationships between a corpus of texts and the words within them. Though it excels at finding connections between documents, it ignores the sequence of words. Although these strategies properly ground the essay in its subject area, they do little to address the essay's ambiguous language. Latent semantic indexing (LSI), a kind of latent semantic analysis (LSA), is the process of examining documents to extract their hidden meanings and ideas [12]. Even though LSA was not designed to be a teaching aid, it was easy to see how its power to assess lexical similarity could be used for essay grading. A high degree of semantic similarity between the essay and the text (syllabus) is desirable given that essays are usually intended to evaluate a student's knowledge and that this knowledge is often acquired through examining a text. Since LSA is not dependent on any particular auxiliary structures, it can be used to extract the meaning and substance of documents expressed in any language. One major drawback of LSA is polysemy. This means it cannot serve as a representation of the term if it can have multiple interpretations [13]. Ontologies provide a common language for describing the meaning of a topic, so information can now be automatically processed to figure out what it means. Communities concerned with lexical similarity have taken a keen interest in ontology in recent years [14]. This is due to its provision of a conceptualization of knowledge that is linked through semantic connections. The use of ontology is not without its restrictions. One of these is the challenge of effectively moving information learned from text to more abstract and philosophical concepts. Second, ontology has trouble dealing with and comprehending ideas whose meaning and connections are unclear. In this way, fuzzy theory can be extremely useful. In contrast to the clear-cut ideas of a topic ontology, a fuzzy ontology includes a wider range of possible interpretations. For the purposes of resolving uncertainty reasoning issues, domain knowledge descriptions are preferable over ontologies of the domain [15; 16; 17; 18]. With the help of fuzzy ontology, the search can be broadened to include all relevant concepts, eliminating the need for exact terminology in order to obtain a usable result (since the context of a document need not be identical for the user to learn from it).