Deep-Time Knowledge Base: Enrich Machine-Readable Semantics and Support Data-Driven Discovery

Xiaogang Ma; Chris McVickar; Jiyin Zhang; Xiang Que; Amruta Kale

doi:10.5281/zenodo.6842475

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Conference proceeding

Deep-Time Knowledge Base: Enrich Machine-Readable Semantics and Support Data-Driven Discovery

Xiaogang Ma, Chris McVickar, Jiyin Zhang, Xiang Que and Amruta Kale

Zenodo (CERN European Organization for Nuclear Research)

07/15/2022

DOI: https://doi.org/10.5281/zenodo.6842475

Abstract

Deep time

Data-driven discovery

Open data

Knowledge graph

This poster presents our latest progress on the construction and implementation of the deep-time knowledge base (deeptimekb.org). This presentation is based upon work supported by the National Science Foundation under Grant No. 1835717

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Title: Deep-Time Knowledge Base: Enrich Machine-Readable Semantics and Support Data-Driven Discovery
Creators: Xiaogang Ma - University of Idaho
Chris McVickar - University of Idaho
Jiyin Zhang - University of Idaho
Xiang Que - University of Idaho
Amruta Kale - University of Idaho
Publication Details: Zenodo (CERN European Organization for Nuclear Research)
Publisher: Zenodo
Identifiers: 996651653701851
Academic Unit: Idaho Experimental Partnership to Stimulate Competitive Research; Initiative for Bioinformatics and Evolutionary Studies; Institute for Health in the Human Ecosystem; Computer Science; Institute for Modeling Collaboration and Innovation
Language: English
Resource Type: Conference proceeding