Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Mining Generalized Associations of Semantic Relations from Textual Web Content
IEEE Transactions on Knowledge and Data Engineering
Using annotations from controlled vocabularies to find meaningful associations
DILS'07 Proceedings of the 4th international conference on Data integration in the life sciences
Dense subgraphs with restrictions and applications to gene annotation graphs
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
InterOnto --- ranking inter-ontology links
DILS'12 Proceedings of the 8th international conference on Data Integration in the Life Sciences
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There is significant knowledge captured through annotations on the life sciences Web. In past research, we developed a methodology of supportand confidencemetrics from association rule mining, to mine the association bridge(of termlinks) between pairs of controlled vocabulary (CV) terms across two ontologies. Our (naive) approach did not exploit the following: implicit knowledge captured via the hierarchical is-astructure of ontologies, and patterns of annotation in datasets that may impact the distribution of parent/child or sibling CV terms. In this research, we consider this knowledge. We aggregate termlinksover the siblings of a parent CV term and use them as additional evidence to boost supportand confidencescores in the associations of the parent CV term. A weight factor (茂戮驴) reflects the contribution from the child CV terms; its value can be varied to reflect a variance of confidence values among the sibling CV terms of some parent CV term. We illustrate the benefits of exploiting this knowledge through experimental evaluation.