One of the most time-consuming tasks that researchers usually have to undergo is finding existing, relevant papers to study and cite in their articles. Manual effort that involves searching relevant papers using keywords not only is time-consuming, but also yields low recall. To mitigate these issues, many automatic citation recommendation methods that find possible citations, using a matrix to represent citation graph, and extracting features to predict citations relevant to the input article, have been proposed. A majority of these methods, however, are proximity-based, which lack global knowledge of the entire citation graph. In this paper, we present a preliminary investigation on a novel approach to recommend citations via knowledge graph embedding. Specifically, ConvCN, an extension of ConvKB algorithm designed for citation knowledge graph embedding, is proposed. We evaluate our approach against the state-of-the-art baselines on WN18RR dataset and citation datasets. The empirical results, using the link prediction protocol, show that the proposed method outperforms all baseline methods in all datasets.