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.
SC-CoMIcs, an MI information extraction corpus, is now ready to make it publicly available. The corpus is on our resource page. Licenses of the data are different between data types. See the “Resources” page from the upper right link on this page.
Kohei MAKINO, Makoto MIWA, Kohei SHINTANI, Atsuji ABE and Yutaka SASAKI, Surrogate modeling of vehicle dynamics using Recurrent Neural Networks, Transactions of the Japan Society of Mechanical Engineers, 2020. (in Japanese) (accepted)