The Whyis Knowledge Graph Framework in Action
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Authors
McCusker, Jamie
Rashid, Sabbir
Agu, Nkechinyere
Bennett, Kristin P.
McGuinness, Deborah L.
Issue Date
2018-10-01
Type
Language
Keywords
Whyis
Alternative Title
Abstract
We will demonstrate a reusable framework for developing knowledge graphs that supports general, open-ended development of knowledge curation, interaction, and inference. Knowledge graphs need to be easily maintainable and usable in sometimes complex application settings. Often, scaling knowledge graph updates can require developing a knowledge curation pipeline that either replaces the graph wholesale whenever updates are made, or requires detailed tracking of knowledge provenance across multiple data sources. Fig. 1 shows how Whyis provides a semantic analysis ecosystem: an environment that supports research and development of semantic analytics for which we previously had to build custom applications [3,4]. Users interact through a suite of knowledge graph views driven by the node type and view requested in the URL. Knowledge curation methods include Semantic ETL, external linked data mapping,and Natural Language Processing (NLP). Autonomous inference agents expand the available knowledge using traditional deductive reasoning as well as inductive methods that can include predictive models, statistical reasoners, and machine learning. Whyis is used in a number of areas today, including nanopolymers, spectrum policy, and health informatics. We demonstrate Whyis by creating and deploying an example Biological Knowledge Graph (BioKG), using data from DrugBank and Uniprot1, and briefly discuss benefits of using our approach over a conventional knowledge graph pipeline.