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<pre>
                  14 True Facts About Knowledge Graphs

                               Bogdan Arsintescu, Scott Meyer, and Swee Lim
                                                 LinkedIn
                                           San Jose, California
                                        barsintescu@linkedin.com
                                                         LinkedIn




Abstract
Knowledge Graphs have been sprouting in the past decade driving a wedge between the traditional relational
database and IR based indices. The fundamental change is modelling data as a graph, with first class relationships
between entities, where edges separate them from relational databases tables and nodes strong identities separate
them from word based IR indices. We present a survey of the known knowledge graphs and the application classes
that they enable, including the LinkedIn Economic Graph. We discuss how graph databases differ from relational
ones and IR indices and where the graph databases would benefit from IR techniques. We conclude with a set
of challenges we see in our experience in scaling Knowledge graphs and graph databases in low latency on-line
applications.

Bio
Bogdan Arsintescu has worked with graphs and semantic data throughout his whole career, most recently at
Google Knowledge Graph leading the graph query language team, in Google Research working on semantic
trajectories using location data and at LinkedIn as a manager in the graph database team. He received his Ph.D.
in CS from Technical University Delft, the Netherlands and MSc in EE from Politehnica University in Bucharest,
Romania.




Copyright c by the paper’s authors. Copying permitted for private and academic purposes.
In: L. Dietz, C. Xiong, E. Meij (eds.): Proceedings of the First Workshop on Knowledge Graphs and Semantics for Text Retrieval
and Analysis (KG4IR), Tokyo, Japan, 11-Aug-2017, published at http://ceur-ws.org




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