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A. Revenko, R. Sabou, A. Ahmeti, M. Schauer:
"Crowd-Sourced Knowledge Graph Extension: A Belief Revision Based Approach.";
Talk: 6th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Zurich, Switzerland; 07-05-2018 - 07-08-2018; in: "Proceedings of the 6th AAAI Conference on Human Computation and Crowdsourcing (HCOMP)", AAAI, (2018), ISBN: 978-1-57735-799-5.

Knowledge graphs are gaining popularity as key ingredients
of many advanced applications. Some are created by experts
covering specific fields. However, for many applications there
is a need of having the common sense knowledge that is
not domain specific, and, therefore, can be provided by nonexperts. In this paper we introduce a novel crowd-sourcing
approach that allows the user to provide their update in a simplistic intuitive form without having the information about
the knowledge already contained in the graph. The approach
roots in belief revision theory and is capable of analyzing the
user input, identifying the compliance with the existing structure and singling out new suggestions. When providing the
update and upon submission the users obtain intuitive colorcoded feedback on their input w.r.t. to consistency and discrepancies with the existing knowledge. This feedback enables the educational aspect of the approach. The approach
guarantees the consistency of the crowd-sourced knowledge
when it is being integrated into the knowledge graph.