通过类别划分提升知识图谱嵌入的语义信息与可扩展性
A Semantic Partitioning Method for Large-Scale Training of Knowledge Graph Embeddings
- 基于类别的知识图谱分块，融合本体信息增强语义表达
- 支持大规模训练，在多个基准上表现优于现有方法
- 适合需要高语义精度的大规模知识图谱应用
近年来，知识图谱嵌入取得了显著进展，诸多方法在各类任务中达到顶尖水平。然而，当前多数方法存在以下问题：(i) 仅考虑事实三元组，忽视知识图谱的本体信息；(ii) 得到的嵌入语义信息不足，难以用于语义任务；(iii) 不支持大规模训练。本文提出一种新算法，将知识图谱的本体信息纳入建模，并基于类进行图划分，以在并行训练中融入更多语义信息，实现大规模知识图谱嵌入训练。初步实验结果表明，该方法在多个主流基准测试中表现良好。
原文摘要 · Abstract (English)
In recent years, knowledge graph embeddings have achieved great success. Many methods have been proposed and achieved state-of-the-art results in various tasks. However, most of the current methods present one or more of the following problems: (i) They only consider fact triplets, while ignoring the ontology information of knowledge graphs. (ii) The obtained embeddings do not contain much semantic information. Therefore, using these embeddings for semantic tasks is problematic. (iii) They do not enable large-scale training. In this paper, we propose a new algorithm that incorporates the ontology of knowledge graphs and partitions the knowledge graph based on classes to include more semantic information for parallel training of large-scale knowledge graph embeddings. Our preliminary results show that our algorithm performs well on several popular benchmarks.
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