Enabling Cognitive Mechanisms in Web-scale Reasoning

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has title::Enabling Cognitive Mechanisms in Web-scale Reasoning
status: ongoing
Master: project within::Knowledge Technology and Intelligent Internet Applications
Student name: student name::Martijn Brakenhoff
number: student number::1507478
Dates
Start start date:=2009/04/01
End end date:=2009/11/20
Supervision
Supervisor: Annette ten Teije
Second reader: has second reader::Frank van Harmelen
Company: has company::Center for Adaptive Behavior and Cognition - Max Plank Institute for Human Development - Berlin
Poster: has poster::Media:Enabling_Cognitive_Mechanisms_Poster.pdf

Signature supervisor



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Abstract

Abstract 1

The Semantic Web aims to make the World Wide Web machine readable, but reasoning isn't very scaleble. The LarKC platform hopes to enable reasoning at web-scale. The platform has to deal with large and messy data-sets. We humans have to do the same every day. In this thesis we will combine the LarKC platform with a cognitive architecture called ACT-R. We will perform a high-level analysis of the two architectures, attempt to find a mapping from one architecture to the other and implement it.

Abstract 2

The Semantic Web aims to make the World Wide Web machine readable, but reasoning doesn't scale easily. The Large Knowledge Collider Project (LarKC) aims to provide a platform for reasoning at web-scale. The platform has to deal with multiple, large, messy and inconsistent data-sets. The human mind faces similar problems every day.

In this thesis we integrate the LarKC platform with a cognitive architecture in order to examine aspects of human reasoning that might be beneficial when reasoning at web-scale. We performed a high-level analysis of the two architectures, briefly explored and experimented with human stopping rules. We propose and implement a prototype integration, create a use-case and add stopping rules based on our experiments.