Supporting the Exploration of Semantic Features in Academic Literature using Graph-based Visualizations (original) (raw)
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Researchers must be aware of the current developments and novel findings in their field. To gain an overview of relevant prior work and ongoing research, academics must review the published literature. However, the search for related academic literature is tedious. Furthermore, researchers can easily oversee potentially valuable information in today’s increasing volume of academic literature. While academic search and recommendation engines have greatly simplified the information acquisition process, current recommendation approaches are not adequately considering specialized semantic similarity measures, such as citation-based similarity, mathematical formulae-based similarity, or image-based similarity to recommend and visualize academic literature. This paper proposes to take into account combinations of semantic features that have previously not been considered for the use case of literature recommendation. Additionally, supporting researchers in the sense-making of semantic sim...
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