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Knowledge-Based Integration of Neuroscience Data Sources
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ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
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Stack-based algorithms for pattern matching on DAGs
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Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Discovering informative connection subgraphs in multi-relational graphs
ACM SIGKDD Explorations Newsletter
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IEEE Transactions on Knowledge and Data Engineering
MonetDB/XQuery: a fast XQuery processor powered by a relational engine
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The VLDB Journal — The International Journal on Very Large Data Bases
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Discovering and using semantics for database schemas
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Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data
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Keyword search on structured and semi-structured data
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Data & Knowledge Engineering
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This paper presents BIODB, an ontology-enhanced information system to manage heterogeneous data. An ontology-enhanced system is a system where ad hoc data is imported into the system by a user, annotated by the user to connect the data to an ontology or other data sources, and then all data connected through the ontology can be queried in a federated manner. The BIODB system enables multi-model data federation, i.e., it federate data that can be in different data models including, relational, XML and RDF, sequence data and so on. It uses an ontologically enhanced system catalog, an ontological data index, an association index to facilitate cross-model data mapping, and a new algorithm for ontology-assisted keyword queries with ranking. The paper describes these components in detail, and presents an evaluation of the architecture in the context of an actual application.