Summary of Distance-adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation, by Weihua Wang and Qiuyu Liang and Feilong Bao and Guanglai Gao
Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
by Weihua Wang, Qiuyu Liang, Feilong Bao, Guanglai Gao
First submitted to arxiv on: 5 Dec 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 This paper proposes a novel quaternion-based knowledge graph embedding model that combines semantic matching and geometric distance scoring functions to measure the plausibility of triplets. The proposed model, dubbed DaBR (Distance Adaptive Bidirectional Rotation), learns rich semantic features by performing right rotations on head entities and reverse rotations on tail entities in the quaternion space. Additionally, it utilizes distance adaptive translations to learn geometric distances between entities. The authors provide mathematical proofs demonstrating their model’s ability to handle complex logical relationships. Experimental results on well-known knowledge graph completion benchmark datasets show that DaBR outperforms previous models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to understand and analyze large amounts of information about people, places, and things (knowledge graphs). Current methods for this task have some limitations. The authors propose a new approach called DaBR that combines two ways of measuring how well related pieces of information fit together. This allows their model to learn more meaningful relationships between entities in the knowledge graph. They test their approach on several benchmark datasets and show that it performs better than existing methods. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph
