Survey on Semantic Interpretation of Tabular Data: Challenges and Directions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913580262096896 |
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| author | Cremaschi, Marco Spahiu, Blerina Palmonari, Matteo Jimenez-Ruiz, Ernesto |
| author_facet | Cremaschi, Marco Spahiu, Blerina Palmonari, Matteo Jimenez-Ruiz, Ernesto |
| contents | Tabular data plays a pivotal role in various fields, making it a popular format for data manipulation and exchange, particularly on the web. The interpretation, extraction, and processing of tabular information are invaluable for knowledge-intensive applications. Notably, significant efforts have been invested in annotating tabular data with ontologies and entities from background knowledge graphs, a process known as Semantic Table Interpretation (STI). STI automation aids in building knowledge graphs, enriching data, and enhancing web-based question answering. This survey aims to provide a comprehensive overview of the STI landscape. It starts by categorizing approaches using a taxonomy of 31 attributes, allowing for comparisons and evaluations. It also examines available tools, assessing them based on 12 criteria. Furthermore, the survey offers an in-depth analysis of the Gold Standards used for evaluating STI approaches. Finally, it provides practical guidance to help end-users choose the most suitable approach for their specific tasks while also discussing unresolved issues and suggesting potential future research directions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_11891 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Survey on Semantic Interpretation of Tabular Data: Challenges and Directions Cremaschi, Marco Spahiu, Blerina Palmonari, Matteo Jimenez-Ruiz, Ernesto Artificial Intelligence Information Retrieval Tabular data plays a pivotal role in various fields, making it a popular format for data manipulation and exchange, particularly on the web. The interpretation, extraction, and processing of tabular information are invaluable for knowledge-intensive applications. Notably, significant efforts have been invested in annotating tabular data with ontologies and entities from background knowledge graphs, a process known as Semantic Table Interpretation (STI). STI automation aids in building knowledge graphs, enriching data, and enhancing web-based question answering. This survey aims to provide a comprehensive overview of the STI landscape. It starts by categorizing approaches using a taxonomy of 31 attributes, allowing for comparisons and evaluations. It also examines available tools, assessing them based on 12 criteria. Furthermore, the survey offers an in-depth analysis of the Gold Standards used for evaluating STI approaches. Finally, it provides practical guidance to help end-users choose the most suitable approach for their specific tasks while also discussing unresolved issues and suggesting potential future research directions. |
| title | Survey on Semantic Interpretation of Tabular Data: Challenges and Directions |
| topic | Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2411.11891 |