=Paper=
{{Paper
|id=Vol-1644/keynote1
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1644/keynote1.pdf
|volume=Vol-1644
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1644/keynote1.pdf</pdf>
<pre>
Combining Statistics and Semantics to Turn
          Data into Knowledge

                                Lise Getoor

                    University of California Santa Cruz
                          getoor@soe.ucsc.edu



 Abstract. Addressing inherent uncertainty and exploiting structure are
 fundamental to turning data into knowledge. Statistical relational learn-
 ing (SRL) builds on principles from probability theory and statistics
 to address uncertainty while incorporating tools from logic to represent
 structure. In this talk I will overview our recent work on probabilistic soft
 logic (PSL), an SRL framework for collective, probabilistic reasoning in
 relational domains. PSL is able to reason holistically about both entity
 attributes and relationships among the entities, along with ontological
 constraints. The underlying mathematical framework supports extremely
 efficient inference. Our recent results show that by building on state-of-
 the-art optimization methods in a distributed implementation, we can
 solve large-scale knowledge graph extraction problems with millions of
 random variables orders of magnitude faster than existing approaches.

</pre>