Ontology mapping: the state of the art
The Knowledge Engineering Review
Automatic Fuzzy Ontology Generation for Semantic Web
IEEE Transactions on Knowledge and Data Engineering
Learning trust strategies in reputation exchange networks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
A survey of trust in computer science and the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Mining Fuzzy Domain Ontology from Textual Databases
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Towards ontology learning from folksonomies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Towards automatic merging of domain ontologies: The HCONE-merge approach
Web Semantics: Science, Services and Agents on the World Wide Web
Learning Useful Kick-off Ontologies from Query Logs: HCOME Revised
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
Abstract framework for social ontologies and folksonomized ontologies
SWIM '12 Proceedings of the 4th International Workshop on Semantic Web Information Management
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Automatically learned social ontologies are products of social fermentation between users that belong in communities of common interests (CoI), in open, collaborative and communicative environments. In such a setting, social fermentation ensures automatic encapsulation of agreement and trust of the shared knowledge of participating stakeholders during an ontology learning process. The paper discusses key issues for trusting the automated learning of social ontologies from social data and furthermore it presents a framework that aims to capture the interlinking of agreement, trust and the learned domain conceptualizations that are extracted from such a type of data. The motivation behind this work is an effort towards supporting the design of new methods for learning trusted ontologies from social content i.e. methods that aim to learn not only the domain conceptualizations but also the degree that agents (software and human) may trust them or not.