Authors:
Guillaume Surroca
1
;
Philippe Lemoisson
2
;
Clément Jonquet
1
and
Stefano Cerri
1
Affiliations:
1
Robotics Micro-Electronics of Montpellier, France
;
2
CIRAD, France
Keyword(s):
Knowledge Representation, Knowledge Discovery and Dissemination, Serendipity, User-Centered Knowledge Engineering, Collective Intelligence, Web 2.0, Agents.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
The Web currently stores two types of content. These contents include linked data from the semantic Web and user contributions from the social Web. Our aim is to represent simplified aspects of these contents within a unified topological model and to harvest the benefits of integrating both content types in order to prompt collective learning and knowledge discovery. In particular, we wish to capture the phenomenon of Serendipity (i.e., incidental learning) using a subjective knowledge representation formalism, in which several “viewpoints” are individually interpretable from a knowledge graph. We prove our own Viewpoints approach by evidencing the collective learning capacity enabled by our approach. To that effect, we build a simulation that disseminates knowledge with linked data and user contributions, similar to the way the Web is formed. Using a behavioral model configured to represent various Web navigation strategies, we seek to optimize the distribution of preference systems
. Our results outline the most appropriate strategies for incidental learning, bringing us closer to understanding and modeling the processes involved in Serendipity. An implementation of the Viewpoints formalism kernel is available. The underlying Viewpoints model allows us to abstract and generalize our current proof of concept for the indexing of any type of data set.
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