Combining fuzzy information from multiple systems (extended abstract)
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Communications of the ACM
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Data Quality Requirements Analysis and Modeling
Proceedings of the Ninth International Conference on Data Engineering
A framework for analysis of data freshness
Proceedings of the 2004 international workshop on Information quality in information systems
Data quality assessment from the user's perspective
Proceedings of the 2004 international workshop on Information quality in information systems
Optimizing Access Cost for Top-k Queries over Web Sources: A Unified Cost-Based Approach
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
| Hi-index | 0.00 |
As more and more Web documents are getting tagged, using tags to enhance the quality of retrieval has become an emerging challenge in the retrieval. In a clear contrast to traditional retrieval techniques solely focusing on content-based relevance to query keyword, we aim at enhancing the retrieval with additional contextual information obtained from tags, e.g., popularity or reliability of the document. Toward the goal, this paper views a tag as a "quality indicator" and classifies tags with various characteristics adopting the taxonomy proposed in data quality modeling literatures. We then develop a unified framework for supporting arbitrary quality indicators in the taxonomy and evaluate its effectiveness and efficiency over both real-life and synthetic data.