However, to evaluate the semantic relatedness between two triples,<cite> Zhang et al. (2014)</cite> primarily relied on the text of triples and used search engines, which makes their method difficult to re-implement and in turn limits its application in practice. Moreover, they did not carry out any task-based evaluation, which makes it uncertain whether their method will be helpful in real applications. Therefore, we instead use topic models, especially Latent Dirichlet Allocation (LDA), to encode distributional semantics of words and convert every triple into a real-valued vector, which is then used to evaluate the relatedness between a pair of triples. We then incorporate these triples into the given source document and represent them together as a graph of triples. Then a modified iterative propagation is carried out over the entire graph to select the most relevant triples of background knowledge to the given source document.