Summary of Survey on Semantic Interpretation Of Tabular Data: Challenges and Directions, by Marco Cremaschi et al.
Survey on Semantic Interpretation of Tabular Data: Challenges and Directions
by Marco Cremaschi, Blerina Spahiu, Matteo Palmonari, Ernesto Jimenez-Ruiz
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive overview of Semantic Table Interpretation (STI), which involves annotating tabular data with ontologies and entities from background knowledge graphs. STI automation enables the construction of knowledge graphs, enrichment of data, and improvement of web-based question answering. The authors categorize approaches using a taxonomy of 31 attributes, allowing for comparisons and evaluations. They also examine available tools based on 12 criteria, providing an in-depth analysis of Gold Standards used for evaluating STI approaches. Additionally, the survey offers practical guidance to help end-users choose suitable approaches for their tasks, while discussing unresolved issues and suggesting potential future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to make tables on the internet more useful by adding important information from background knowledge graphs. This helps create bigger pictures of data, makes it easier to find answers online, and improves how well computers can understand questions. The authors look at different ways people do this job, compare their methods using 31 characteristics, and examine special tools that help with the task. They also talk about what makes a good standard for measuring how well these approaches work. |
Keywords
» Artificial intelligence » Question answering
