Artificial learning approaches for the next generation Web: Part I

Abstract

IN THIS PAPER WE PRESENT A REVIEW OF LEARNING APPROACHES THAT HAVE BEEN USED BY THE RESEARCH COMMUNITY TO CARRY OUT CLUSTERING AND PATTERN RECOGNITION TASKS. ARTIFICIAL NEURAL NET WORKS ARE THEN INTRODUCED BY PRESENTING EXISTING TOPOLOGIES, LEARNING ALGORITHMS, AND RECALL APPROACHES. FINALLY, THE RELATION OF THESE TECHNIQUES WITH THE SEMANTIC WEB ONTOLOGY CREATION PROCESS, AS WE EN VISION IT, IS INTRODUCED. IN PART II OF THIS PAPER, AN ARTIFICIAL LEARNING APPROACH BASED ON SELF-ORGANIZING MAPS (SOM) THAT WE HAVE PROPOSED AS AN ONTOLOGY LEARNING TOOL FOR ASSEMBLING AND VISUALIZING ONTOLOGY COMPONENTS FROM A SPECIFIC DO MAIN FOR THE SE MAN TIC WEB IS INTRODUCED