A Display of Conceptual Structures in the Epidemiologic Literature

Biomedical literature from PubMed contains various types of entities such as diseases or organisms. The rapid growth of their size makes it harder to conceptualized; however, displaying the natural terms that occurred in the text is more effective in understanding the target corpus to be searched than suggesting a concept related to a user query. Thus, we consider the natural common words that biomedical information users actually write and speak. We extract bio-related terms from the corpus mapping with the UMLS. We show entity-based networks with natural language terms as they are shown in the text. In this paper, we present simple and precise associative networks of natural terms in the biomedical literature. The entity-based networks and entity relations can make understanding the biomedical literature corpus more effective and easier by detecting related terms and their hidden relations in the documents. We considered bio-entities and their relations in the biomedical literature and focused on the representation of a graphic display that can improve users' perception about a large corpus. To this end, epidemiology as an experimental domain was chosen and we extract entities from the corpus mapping the UMLS and draw their relations inferred by the Semantic Network of the UMLS. Then we calculate term frequencies, co-occurrences, and term pair similarities (See Figure 1). In results, distinguished networks that display conceptual structures in the biomedical literature with a natural language and not a concept were demonstrated (See Figure 2). The networks we present provide more comprehension of the biomedical collection.