Cold Start Cumulative Citation Recommendation for Knowledge Base Acceleration.

This paper studies cold start Cumulative Citation Recommen dation (CCR) for Knowledge Base Acceleration (KBA), whose objective is to detect potential citations for target entities without existing KB entries from a volume of stream documents. Unlike routine CCR, in which target entities are identified by a reference KB, cold start CCR is more common since lots of less popular entities do not have any KB entry in practice. We propose a two-step strategy to address this problem: (1) event-based sentence clustering and (2) document ranking. In addition, to build effective rankers, we develop three kinds of features based on the clustering results: time range, local profile and action pattern. Empirical studies on TREC-KBA-2014 dataset demonstrate the effectiveness of the proposed strategy and the novel features.