Summary of Query-enhanced Adaptive Semantic Path Reasoning For Inductive Knowledge Graph Completion, by Kai Sun et al.
Query-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge Graph Completion
by Kai Sun, Jiapu Wang, Huajie Jiang, Yongli Hu, Baocai Yin
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper proposes the Query-Enhanced Adaptive Semantic Path Reasoning (QASPR) framework for inductive Knowledge Graph Completion (KGC), which can handle emerging entities and relations in incomplete Knowledge Graphs. Existing inductive KGC methods struggle with noisy structural information during reasoning and capturing long-range dependencies. QASPR addresses these challenges by introducing a query-dependent masking module to retain important information and a global semantic scoring module to evaluate the collective impact of nodes along reasoning paths. The proposed framework achieves state-of-the-art performance on various benchmarks, demonstrating its effectiveness in enhancing KGC tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big book with lots of information about different things, like people, places, and ideas. Sometimes, this book might be missing some important details or connections between these things. This paper talks about how to fill in those gaps using artificial intelligence techniques called Knowledge Graph Completion (KGC). The problem is that traditional methods for doing this aren’t very good at handling new information that comes up suddenly. To solve this issue, the authors propose a new method called QASPR, which is better at adapting to new information and ignoring noisy data. This method seems to work really well, according to tests on various datasets. |
Keywords
» Artificial intelligence » Knowledge graph
