Summary of A Contextualized Bert Model For Knowledge Graph Completion, by Haji Gul et al.
A Contextualized BERT model for Knowledge Graph Completion
by Haji Gul, Abdul Ghani Naim, Ajaz A. Bhat
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 introduces a contextualized BERT model for Knowledge Graph Completion (KGC) that predicts missing nodes or links in knowledge graphs, enhancing their informational depth and utility. The model leverages neighboring entities and relationships to predict tail entities, eliminating the need for entity descriptions and negative triplet sampling. This approach reduces computational demands while improving performance, outperforming state-of-the-art methods on standard datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to complete knowledge graphs by using information from nearby things in the graph. The model helps fill in gaps by looking at what’s around the missing parts. This makes it more efficient and accurate than other approaches that rely too much on descriptions of individual things or require lots of extra work. |
Keywords
* Artificial intelligence * Bert * Knowledge graph
