=Paper=
{{Paper
|id=Vol-230/paper-3
|storemode=property
|title=Statistical Relational Learning - A Logical Approach
|pdfUrl=https://ceur-ws.org/Vol-230/03-deraedt.pdf
|volume=Vol-230
|dblpUrl=https://dblp.org/rec/conf/ijcai/Raedt07
}}
==Statistical Relational Learning - A Logical Approach==
<pdf width="1500px">https://ceur-ws.org/Vol-230/03-deraedt.pdf</pdf>
<pre>
                                    Invited keynote talk:


       Statistical Relational Learning – A Logical Approach
                                     Luc de Raedt
                             Katholieke Universiteit Leuven
                                3001 Heverlee, Belgium


ABSTRACT

In this talk I will briefly outline and survey some developments in the field of statistical
relation learning, especially focussing on logical approaches. Statistical relational
learning is a novel research stream within artificial intelligence that combines principles
of relational logic, learning and probabilistic models. This endeavor is similar in spirit to
the developments in Neural Symbolic Reasoning in that it attempts to integrate symbolic
representation and reasoning methods with the advantages of subsymbolic
representations. In the talk, I shall attempt to make this link more explicit and to present
an overview of the state of the art in Statistical Relational Learning. This overview shall
start by providing some background in logical approaches to learning (relational learning
and inductive logic programming) and then extend it with probabilistic elements.

</pre>