Abstract
Extracting semantics, entities and patterns from collections or the semantic web is a knowledge discovery process, which can be automated by using multi-agent systems. Such multi –agent systems are intelligent and collaborative software agents that can be orchestrated into a federation of agents with the scope of knowledge discovery. This paper presents an integrated methodology for knowledge discovery in data warehouses that have been populated from various sources such as semantic web, databases, and social media. The proposed methodology and the Knowledge Discovery Multi-agent System (KDMAS) are based on an innovative process oriented design, method that gives a holistic view of the system components and it combines agent engineering, ontology engineering and data mining techniques. The case study and the domain used to evaluate KDMAS is Cultural Heritage (CH).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
