Authors:
Jayanta Poray
and
Christoph Schommer
Affiliation:
University of Luxembourg, Luxembourg
Keyword(s):
Adaptive information management, Learning, Associative memories, Linguistic processing, Graph operations.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Cognitive Systems
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Expert Systems
;
Health Information Systems
;
Human-Computer Interaction
;
Intelligent User Interfaces
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
Mind-graphs define an associative-adaptive concept of managing information streams, like for example words
within a conversation. Being composed of vertices (or cells; representing external stimuli like words) and undirected
edges (or connections), mind-graphs adaptively reflect the strength of simultaneously occurring stimuli
and allow a self-regulation through the interplay of an artificial ‘fever’ and ‘coldness’ (capacity problem).
With respect to this, an interesting application scenario is the merge of information streams that derive from a
conversation of k conversing partners. In such a case, each conversational partner has an own knowledge and
a knowledge that (s)he shares with other. Merging the own (inside) and the other’s (outside) knowledge leads
to a situation, where things like e.g. trust can be decided. In this paper, we extend this concept by proposing
extended mind-graph operations, dealing with the merge of sub-mind-graphs and the extraction of mind-graph
skeletons.