Summary of Kglink: a Column Type Annotation Method That Combines Knowledge Graph and Pre-trained Language Model, by Yubo Wang et al.
KGLink: A column type annotation method that combines knowledge graph and pre-trained language model
by Yubo Wang, Hao Xin, Lei Chen
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 proposed method leverages knowledge graphs (KGs) and deep learning techniques to annotate tabular data at the column level. The authors aim to overcome limitations in previous KG-based approaches, which struggle when there are no matches between column cells and the KG. This issue can lead to multiple predictions for a single column, making it difficult to determine the most suitable semantic type granularity. By integrating deep learning and KGs, the method addresses this scalability limitation, enabling more accurate and efficient annotation of tabular data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new approach to annotate tabular data by combining knowledge graphs (KGs) with deep learning techniques. It solves problems that previous methods had, like when there’s no match between column cells and the KG. This makes it hard to choose the right type for each column. The method is designed to help with these challenges. |
Keywords
» Artificial intelligence » Deep learning
