Baseline systems As<cite> Zhang et al. (2014)</cite> argued, it is difficult to use the methods in traditional ranking tasks, such as information retrieval (Manning et al., 2008) and entity linking (Han et al., 2011; Sen, 2012) , as baselines in this task, because our model takes triples as basic input and thus lacks some crucial information such as link structure. For better comparison, we implement three methods as baselines, which have been proved effective in relevance evaluation: (1) Vector Space Model (VSM), (2) Word Embedding (WE), and (3) Latent Dirichlet Allocation (LDA). Note that our model captures the distributional semantics of triples with LDA, while WE serves as a baseline only, where the word embeddings are acquired over the same corpus mentioned previously with 2 http://www.sogou.com/labs/dl/c.html the publicly available tool word2vec 3 .