Summary of Pathformer: Recursive Path Query Encoding For Complex Logical Query Answering, by Chongzhi Zhang et al.
Pathformer: Recursive Path Query Encoding for Complex Logical Query Answering
by Chongzhi Zhang, Zhiping Peng, Junhao Zheng, Linghao Wang, Ruifeng Shi, Qianli Ma
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Logic in Computer Science (cs.LO)
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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 proposes a novel approach to Complex Logical Query Answering (CLQA) over incomplete knowledge graphs, which involves modeling long-range dependencies between words using transformer architecture. The authors argue that current Query Embedding (QE) methods only consider historical query context information and fail to capture complex dependencies behind the elements of a query. To address this limitation, they introduce Pathformer, a neural one-point embedding method based on tree-like computation graphs. Specifically, Pathformer decomposes the query computation tree into path query sequences and uses transformer encoder to recursively encode these sequences. This approach allows Pathformer to utilize future context information and model complex interactions between various parts of the query. Experimental results show that Pathformer outperforms existing competitive neural QE methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pathfinder is a new way to answer complex questions using incomplete data. Current methods only look at what happened before, but don’t consider what might happen in the future. To solve this problem, the authors propose a new approach called Pathformer. It breaks down complex queries into smaller parts and uses a special kind of computer program (called transformer) to understand how these parts relate to each other. This allows Pathformer to take into account both past and future information when answering questions. The results show that Pathformer is better than existing methods at answering complex questions. |
Keywords
» Artificial intelligence » Embedding » Encoder » Transformer
