On the use of spreading activation methods in automatic information
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Usage patterns of collaborative tagging systems
Journal of Information Science
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
Using annotations in enterprise search
Proceedings of the 15th international conference on World Wide Web
cloudalicious: folksonomy over time
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Information retrieval with commonsense knowledge
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Query expansion with conceptnet and wordnet: an intrinsic comparison
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Hi-index | 0.00 |
Social bookmark services like del.icio.us enable easy annotation for users to organize their resources. Collaborative tagging provides useful index for information retrieval. However, lack of sufficient tags for the developing documents, in particular for new arrivals, hides important documents from being retrieved at the earlier stages. This paper proposes a spreading activation approach to predict social annotation based on document contents and users' tagging records. Total 28,792 mature documents selected from del.icio.us are taken as answer keys. The experimental results show that this approach predicts 71.28% of a 100 users' tag set with only 5 users' tagging records, and 84.76% of a 13-month tag set with only 1-month tagging record under the precision rates of 82.43% and 89.67%, respectively.