Our paper has been accepted for publication on Frontiers in Research Metrics and Analytics, 2021.
This paper proposes a new Knowledge Graph dataset that includes not only heterogeneous node relations but also textual descriptions. The dataset targets pharmaceutical areas but it can contribute to the Knowledge Graph research community by providing evidences in the usefulness of textural information integrated in a knowledge graph.
Masaki Asada, Gunasekaran Nallappan, Makoto Miwa and Yutaka Sasaki, Representing a Heterogeneous Pharmaceutical Knowledge-Graph with Textual Information, Front. Res. Metr. Anal., doi: 10.3389/frma.2021.670206, 2021.