ConvCN: A CNN-Based Citation Network Embedding Algorithm towards Citation Recommendation

Chanathip Pornprasit, Xin Liu, Natthawut Kertkeidkachorn, Kyoung-Sook Kim, Thanapon Noraset, and Suppawong Tuarob

Abstract:

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.