Authors:
Alexander Smirnov
;
Tatiana Levashova
;
Nikolay Shilov
and
Andrew Ponomarev
Affiliation:
St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39, 14th line, St. Petersburg, 199178 and Russia
Keyword(s):
Human-machine Collective Intelligence, Semantic Interoperability, Multi-aspect Ontology, Decision Support.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Human-Machine Cooperation
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Ontology Sharing and Reuse
;
Symbolic Systems
Abstract:
A collective intelligence system could significantly help to improve decision making. Its advantage is that often collective decisions can be more efficient than individual ones. The paper considers the human-machine collective intelligence as shared intelligence, which is a product of the collaboration between humans and software services, their joint efforts and conformed decisions. Usually, multiple collaborators do not share a common view on the domain or problem they are working on. The paper assumes usage of multi-aspect ontologies to overcome the problem of different views thus enabling humans and intelligent software services to self-organize into a collaborative community for decision support. A methodology for development of the above multi-aspect ontologies is proposed. The major ideas behind the approach are demonstrated by an example from the smart city domain.