Author:
Christoph Schommer
Affiliation:
University Luxembourg, Luxembourg
Keyword(s):
Bio-inspired modeling, Graph mining, Bibliographic communities.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Business Intelligence Applications
;
Data Analytics
;
Data Engineering
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
This paper introduces in extracts a bio-inspired model that understands graphs as artificial chemical constructs. The main objective is to identify this model as an autonomous and adaptive system that performs internal tasks, for example a communication with its environment. The model itself focus on artificial atomicity of nodes, artificial molecular connections in between, and functional proteins, which are self-concentrated constructs. The model implicates a solid fundament, but fosters an artificial vitality through catalysts: these merge attacked atomic nodes – in case of common “interests” (inside the molecular model) – to functional proteins and therefore consequently contribute to a vivid shape of communities. As an application example, the theoretical model is clarified with bibliographic entries to form bibliographic communities dynamically while having a bibliographic stream entries as input.