We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Op-Trans: An Optimization Framework for Negative Sampling and Triplet-Mapping Properties in Knowledge Graph Embedding.
- Authors
Han, Huixia; Li, Xinyue; Wu, Kaijun
- Abstract
Knowledge graphs are a popular research field in artificial intelligence, and store large amounts of real-world data. Since data are enriched over time, the knowledge graph is often incomplete. Therefore, knowledge graph completion is particularly important as it predicts missing links based on existing facts. Currently, the family of translation models delivers a better performance in knowledge graph completion. However, most of these models randomly generate negative triplets during the training process, resulting in the low quality of negative triplets. In addition, such models ignore the important characteristics of triplet-mapping properties during model learning. Therefore, we propose an optimization framework based on the translation models (Op-Trans). It enhances the knowledge-graph completion effect from both negative sampling and triplet-mapping properties. First, we propose a clustering cache to generate negative triplets, which generate negative triplets based on entity similarity. This sampling method can directly use the cache to track the negative triplets with large scores. In addition, we focus on the different contributions of the triplets to the optimization goal. We calculate the distinct weight for each triplet according to its mapping properties. In this way, the scoring function deals with each triplet depending on its own weight. The experimental results show that Op-Trans can help the state-of-the-art baselines to obtain a better performance in a link prediction task.
- Subjects
KNOWLEDGE graphs; ARTIFICIAL intelligence; SAMPLING errors; GRAPH algorithms
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 5, p2817
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13052817