# Ontology-Driven Semantic Analysis of Tabular Data:
## 2 Related Works  
Nowadays, there are several solutions that allow for the semantic analysis of tables. TableMiner+ [5] is a tool designed to automatically extract table structures and content from HTML documents. It detects tables, extracts headers, cells, columns, and rows, and determines the data types in tables. The tool uses an iterative approach to refine the quality of data extraction. However, it may run into problems when working with complex tables, such as tables with graphics or formulas. TableMiner+ works based on certain table formatting rules and requires adjustments for tables that do not follow those rules. It only handles PDF and HTML formats. Despite its limitations, it is effective for semantic data extraction from tables and has applications in areas such as medical statistics and data science. QTLTableMiner++ [6] extracts semantic information from tables in articles related to genetics. It uses natural language processing and machine learning to extract information about genetic variants from tables. The tool defines tables of genetic variant data and links them to the corresponding genetic loci. It is based on the Apache Solr search engine and can index and search text data in multiple languages. However, its use is limited to certain table formats from a particular resource. Overall, it is a powerful tool for extracting genetic data, but it has limitations, such as a limited table format and a domain-specific codebase. Qurma is a pipeline for extracting tables, interpreting and enriching knowledge graphs from various sources, based on the Camelot library [7]. Qurma efficiently processes HTML pages, PDFs, and images initiated by a user-provided document URL to retrieve a table. The result is an optimized dataset that can be exported as CSV, JSON, or attribute values. Qurma's conceptual architecture is based on the Clean Architecture paradigm, with a focus on controlled interfaces and a layered structure. MantisTable is a public tool for semantic annotation of tables from scientific publications [8]. It identifies objects in table cells and links them to knowledge bases like DBpedia using string matching, word embedding, and machine learning. The tool was evaluated on 100 tables from scientific articles, comparing its abstracts with those of experts. It demonstrated high F1 values for object recognition and linking. Compared to other tools, MantisTable showed the best F1 values for both tasks. Its intuitive interface is highly acclaimed by users. Semantic annotations from MantisTable can improve the search and retrieval of scientific content. [PERSON] et al. introduced the concept of the Semantic Web, where data is given a well-defined meaning that allows computers and people to work together [9]. OWL is a key component of this vision, providing a rich language for defining structured web ontologies. The disadvantages of this method are (1) difficult to implement, as the vision of the Semantic Web requires significant changes to the existing web infrastructure, and (2) dependence on widespread adoption; for the Semantic Web to be effective, most web content creators must adopt standards, (3) the possibility semantic inconsistencies; different creators may interpret values differently, resulting in semantic inconsistencies. [PERSON], [PERSON] discussed the problems and techniques involved in interpreting tables in documents, emphasizing the need to understand both structure and semantics [10]. It hasthe following disadvantages: (1) limited to document-based tables--may not be efficient for web tables or dynamic tables; (2) problems handling different table structures and formats. In [11], the authors proposed an approach to interpreting tables using linked data and ontologies. Their method focuses on creating RDF triplets from tables. However, this method has a dependency on the quality and completeness of ontologies and potential problems when matching tables with existing ontologies, especially if they are not aligned.