Summary of Retrieval, Reasoning, Re-ranking: a Context-enriched Framework For Knowledge Graph Completion, by Muzhi Li et al.
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion
by Muzhi Li, Cehao Yang, Chengjin Xu, Xuhui Jiang, Yiyan Qi, Jian Guo, Ho-fung Leung, Irwin King
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Knowledge Graph Completion (KGC) framework, KGR3, aims to infer missing entities by leveraging not only triples but also entity contexts such as labels, descriptions, and aliases. Building upon existing embedding-based methods and text-based approaches, KGR3 consists of three modules: Retrieval, Reasoning, and Re-ranking. The framework retrieves supporting triples from the KG, collects plausible candidate answers, and generates potential answers for each query triple using a large language model. The Re-ranking module combines these candidates and fine-tunes an LLM to provide the best answer. KGR3 demonstrates significant improvements on various KGC methods, with absolute Hits@1 improvements of 12.3% and 5.6% on the FB15k237 and WN18RR datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KGR3 is a new way to help computers complete incomplete information in knowledge graphs. It looks at more than just what’s already known about an entity, but also things like labels, descriptions, and names that might be useful for guessing what’s missing. The method has three steps: finding related information, generating possible answers, and choosing the best one. This helps KGR3 do better than other methods, especially when dealing with tricky or rare cases. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph » Large language model
