Doc2Vec (Le and Mikolov, 2014) is then used on the documents of each data source S i to obtain a local word embedding matrix E i and a local document embedding matrix F i , where each column of E i (F i ) corresponds to a word (document) vector representation of S i .
we use embeddings, E i c and E i o as the local concept representation of c and the local stock representation of o in data source S i , respectively.