Summary of Prompt-fused Framework For Inductive Logical Query Answering, by Zezhong Xu et al.
Prompt-fused framework for Inductive Logical Query Answering
by Zezhong Xu, Peng Ye, Lei Liang, Huajun Chen, Wen Zhang
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach to answering logical queries on knowledge graphs (KGs) is proposed, addressing the challenges posed by incomplete KGs. The primary obstacle is not just missing edges but also the emergence of new entities. Existing methods focus on individual logical operators rather than comprehensively analyzing the query as a whole. To address this, the Pro-QE framework incorporates existing query embedding methods and aggregates contextual information to handle emerging entities. A query prompt is generated to gather relevant information from a holistic perspective. Two new challenging benchmarks are introduced for evaluating the model’s efficacy in the inductive setting. Experimental results demonstrate successful handling of unseen entities in logical queries. The ablation study confirms the importance of the aggregator and prompt components. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a way to answer complex questions on large databases called knowledge graphs. They noticed that these databases often have missing information, which makes it hard to get accurate answers. To solve this problem, they created a new approach called Pro-QE, which can handle both missing and new information. They also came up with a special technique to help the model understand what’s being asked and find relevant information. To test their idea, they created two new challenges for computers to solve. The results showed that their approach works well even when dealing with unknown information. This is an important step forward in developing more powerful question-answering systems. |
Keywords
* Artificial intelligence * Embedding * Prompt * Question answering
