Knowledge graph embedding approaches represent nodes and edges of graphs as mathematical vectors. Current approaches focus on embedding complete knowledge graphs, i.e. all nodes and edges. This leads to very high computational requirements on large graphs such as DBpedia or Wikidata. However, for most downstream application scenarios, only a small subset of concepts is of actual interest. In this paper, we present RDF2Vec Light, a lightweight embedding approach based on RDF2Vec which generates vectors for only a subset of entities. To that end, RDF2Vec Light only traverses and processes a subgraph of the knowledge graph. Our method allows the application of embeddings of very large knowledge graphs in scenarios where such embeddings were not possible before due to a significantly lower runtime and significantly reduced hardware requirements.

中文翻译：

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RDF2Vec Light——一种用于知识图嵌入的轻量级方法

知识图嵌入方法将图的节点和边表示为数学向量。当前的方法侧重于嵌入完整的知识图，即所有节点和边。这导致对大型图（例如 DBpedia 或 Wikidata）的计算要求非常高。然而，对于大多数下游应用场景，只有一小部分概念是真正感兴趣的。在本文中，我们提出了 RDF2Vec Light，这是一种基于 RDF2Vec 的轻量级嵌入方法，它仅为实体的子集生成向量。为此，RDF2Vec Light 只遍历和处理知识图的一个子图。我们的方法允许在以前由于显着降低运行时间和显着降低硬件要求而无法进行此类嵌入的场景中应用非常大的知识图谱的嵌入。