Characterizing Knowledge Entity Extracted from Citation Sentences

This paper proposes a novel entitymetrics analysis by exclusively focusing on citation sentence. Since citation sentence offers both citing and cited author's research interest, knowledge entity that appears in this sentence can be considered as a key entity. To characterize such key entities, we conduct an entitymetrics analysis on citation sentences that are extracted from full-text research articles collected from PMC. We use “opioid” as our search query since it is an actively studied domain, which indicates that rigorous amounts of knowledge entities and entity pairs are available for examination. After which, we construct two novel citation sentence-based networks, namely the direct citation sentence (DCS) network and the indirect citation sentence (ICS) network. The DCS network is built upon direct entity pairs that are captured within citation sentences. The ICS network, on the other hand, utilizes indirect entity cooccurrences based on cited author information that appears inside a citation sentence. To demonstrate the usefulness of the DCS and ICS network, a conventional full-text network is formed for comparison analysis based on network features and opioid-related bio-entity pairs. The results show that DCS and ICS network demonstrate distinct network characteristics and provide unobserved top-ranked bio-entity pairs when compared to traditional method. This indicates that our method can expand the base of entitymetrics and provide new insights for knowledge structure analysis.