Why Wikibase? Why not?

Presented by Ron Snyder and Steve Coffman of ITHAKA/ JSTOR.
sps.columbia.ed...
In recent projects the JSTOR Labs (labs.jstor.org) team has jumped head first into the semantic web pool. Knowledge graphs and LOD are a great fit when the goal is to connect disparate data items which is a common theme in many of the Labs' projects. We’ve explored a few solutions for our knowledge graph infrastructure but have settled on a self-hosted instance of Wikibase, the MediaWiki extension behind Wikidata.
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Offered on Columbia University’s Morningside campus in New York City, the Knowledge Graph Conference (KGC) is a world-class curated program that brings experienced practitioners, technology leaders, cutting-edge researchers, academics and vendors together for two days of presentations, discussions and networking on the topic of knowledge graphs.
While the underlying technologies to store, retrieve, publish and model knowledge graphs have been around for a while, it is only in recent years that widespread adoption has started to take hold.
As knowledge is an essential component of intelligence, knowledge graphs are an essential component of AI. They form an organized and curated set of facts that provide support for models to understand the world. Today, they power tasks like natural language understanding, search and recommendation, and logical reasoning. Tomorrow they will ubiquitously be used to store and retrieve facts learned by intelligent agents.
In the enterprise, knowledge graphs are the ultimate dataset. Integrating and organizing together internal and external data sources. Knowledge graphs integrate with the larger information system: master data management, data governance, data quality. Their flexibility and powerful representation capabilities allow data scientists to tap them to build powerful models.
The Knowledge Graph Conference is coordinated by Columbia University School of Professional Studies' Executive Education program. Visit: sps.columbia.ed... for more information.