论文标题
RDF2VEC LIGHT-一种轻巧的知识图嵌入方法
RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings
论文作者
论文摘要
知识图嵌入方法代表图形的节点和边缘作为数学向量。当前的方法着重于嵌入完整的知识图，即所有节点和边缘。这导致了大图（例如DBPEDIA或WIKIDATA）的计算要求非常高。但是，对于大多数下游应用程序方案，只有一小部分概念是真正的兴趣。在本文中，我们提出了RDF2VEC Light，这是一种基于RDF2VEC的轻巧嵌入方法，该方法仅生成一个部分实体的向量。为此，RDF2VEC光仅遍历和处理知识图的子图。我们的方法允许将非常大知识图的嵌入在场景中应用，在这种情况下，由于运行时间明显降低并大大减少了硬件要求，因此无法在此类嵌入情况下应用。
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.
