Summary of Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-aware Query Representation Learning, by Jeonghoon Kim et al.
Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning
by Jeonghoon Kim, Heesoo Jung, Hyeju Jang, Hogun Park
First submitted to arxiv on: 11 Jun 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 paper proposes a novel methodology to enhance the effectiveness of multi-hop logical reasoning on knowledge graphs. The goal is to answer First-Order Logic (FOL) queries by fully integrating the context of the FOL query graph. The approach involves discerning two types of context: structural context inherent to the query structure and relation-induced context unique to each node in the query graph, as delineated in the corresponding knowledge graph. This dual-context paradigm helps nodes within a query graph attain refined internal representations throughout the multi-hop reasoning steps. The proposed methodology is model-agnostic and can be applied to existing multi-hop logical reasoning approaches. Experiments on two datasets show performance improvements of up to 19.5%. The code for this approach is available at https://github.com/kjh9503/caqr. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to answer complex questions that involve connections between many pieces of information. This paper introduces a new way to make computers better at doing just that. Right now, computers are good at answering simple questions, but they struggle when the question involves many relationships between different pieces of information. The new approach helps computers understand these relationships and use them to answer more complex questions. By using this method, computers can improve their performance by up to 19.5%. You can find the code for this approach online. |
Keywords
» Artificial intelligence » Knowledge graph
