A distributed, semiotic-inductive, and human-oriented approach to web-scale knowledge retrieval

Authors:
Edy Portmann;Michael Alexander Kaufmann;Cédric Graf
Affiliations:
University of California, Berkeley, Berkeley, CA, USA;University of Fribourg, Fribourg, Switzerland;Born Informatik AG, Bern, Switzerland
Venue:
Proceedings of the 2012 international workshop on Web-scale knowledge representation, retrieval and reasoning
Year:
2012

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Abstract

Web-scale knowledge retrieval can be enabled by distributed information retrieval; clustering Web clients to a large-scale computing infrastructure for knowledge discovery from Web documents. Based on this infrastructure, we propose to apply semiotic (i.e., sub-syntactical) and inductive (i.e., probabilistic) methods for inferring concept associations in human knowledge. These associations can be combined to form a fuzzy (i.e., gradual) semantic net representing a map of the knowledge in the Web. Thus, we propose to provide interactive visualizations of these cognitive concept maps to end users, who can browse and search the Web in a human-oriented, visual, and associative interface.