论文标题
旨在利用隐性人类反馈以改善RDF2VEC嵌入
Towards Exploiting Implicit Human Feedback for Improving RDF2vec Embeddings
论文作者
论文摘要
RDF2VEC是一种用于从RDF知识图创建向量空间嵌入的技术，即表示图中的每个实体作为向量。它首先通过在图表上执行随机步行来创建节点序列。在第二步中，这些序列由用于创建实际嵌入的Word2Vec算法处理。在本文中，我们探讨了外部边缘的使用来指导随机步行。作为边缘权重，Wikipedia中页面之间的过渡概率被用作人类反馈边缘重要性的代理。我们表明，在某些情况下，使用这些过渡概率的RDF2VEC可以根据随机步行以及图形内部边缘权重的使用来胜过RDF2VEC。
RDF2vec is a technique for creating vector space embeddings from an RDF knowledge graph, i.e., representing each entity in the graph as a vector. It first creates sequences of nodes by performing random walks on the graph. In a second step, those sequences are processed by the word2vec algorithm for creating the actual embeddings. In this paper, we explore the use of external edge weights for guiding the random walks. As edge weights, transition probabilities between pages in Wikipedia are used as a proxy for the human feedback for the importance of an edge. We show that in some scenarios, RDF2vec utilizing those transition probabilities can outperform both RDF2vec based on random walks as well as the usage of graph internal edge weights.
