Learning to create data-integrating queries
Proceedings of the VLDB Endowment
Flexible and efficient querying and ranking on hyperlinked data sources
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Challenges in personalized authority flow based ranking of social media
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A novel keyword search paradigm in relational databases: Object summaries
Data & Knowledge Engineering
Ranking objects by following paths in entity-relationship graphs
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
Answering complex structured queries over the deep web
Proceedings of the 15th Symposium on International Database Engineering & Applications
Size-l object summaries for relational keyword search
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
Authority flow is an effective ranking mechanism for answering queries on a broad class of data. Systems have been developed to apply this principle on the Web (PageRank and topic sensitive PageRank), bibliographic databases (ObjectRank), and biological databases (Hubs of Knowledge project). However, these systems have the following drawbacks: (a) There is no way to explain to the user why a particular result received its current score; (b) The authority flow rates, which have been shown to dramatically affect the results' quality in ObjectRank, have to be set manually by a domain expert; (c) There is no query reformulation methodology to refine the query results according to the user's preferences. In this work, we address these shortcomings by introducing a framework and algorithms to explain query results and reformulate authority flow queries based on the user's feedback. The query reformulation process can be used to learn the user's preferences and automatically adjust the authority flow rates to facilitate personalized authority flow searching. We experimentally evaluate our algorithms in terms of performance and quality.