论文标题
更多并不总是更好：A-box物质化对RDF2VEC知识图嵌入的负面影响
More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings
论文作者
论文摘要
RDF2VEC是一种嵌入技术，用于代表连续矢量空间中的知识图实体。在本文中，我们研究了由子专业诱导的构造隐式A-box公理的效果，以及对称和及物特性。虽然可能是一个合理的假设，即在计算嵌入之前的实现化可能会导致更好的嵌入，但我们对DBPEDIA进行了一组实验，这表明该物质化实际上对RDF2VEC的性能产生了负面影响。在我们的分析中，我们认为，尽管致力于完成知识图中缺少信息的工作大量工作，但这种丢失的隐式信息实际上是一个信号，而不是缺陷，我们展示的例子说明了这一假设。
RDF2vec is an embedding technique for representing knowledge graph entities in a continuous vector space. In this paper, we investigate the effect of materializing implicit A-box axioms induced by subproperties, as well as symmetric and transitive properties. While it might be a reasonable assumption that such a materialization before computing embeddings might lead to better embeddings, we conduct a set of experiments on DBpedia which demonstrate that the materialization actually has a negative effect on the performance of RDF2vec. In our analysis, we argue that despite the huge body of work devoted on completing missing information in knowledge graphs, such missing implicit information is actually a signal, not a defect, and we show examples illustrating that assumption.
