With the growth in biomedical literature, the necessity of extracting useful information from the literature has increased. One approach to extracting biomedical knowledge involves using citation relations to discover entity relations. The assumption is that citation relations between any two articles connect knowledge entities across the articles, enabling the detection of implicit relationships among biomedical entities. The goal of this article is to examine the characteristics of biomedical entities connected via intermediate entities using citation relations aided by text mining. Based on the importance of symptoms as biomedical entities, we created triples connected via citation relations to identify drug–disease pairs with shared symptoms as intermediate entities. Drug–disease interactions built via citation relations were compared with co‐occurrence‐based interactions. Several types of analyses were adopted to examine the properties of the extracted entity pairs by comparing them with drug–disease interaction databases. We attempted to identify the characteristics of drug–disease pairs through citation relations in association with biomedical entities. The results showed that the citation relation‐based approach resulted in diverse types of biomedical entities and preserved topical consistency. In addition, drug–disease pairs identified only via citation relations are interesting for clinical trials when they are examined using BITOLA.

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