Schema mappings, data exchange, and metadata management
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Survey of semantic annotation platforms
Proceedings of the 2005 ACM symposium on Applied computing
MONDRIAN: Annotating and Querying Databases through Colors and Blocks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
An annotation management system for relational databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A Conceptual Modeling Framework for Expressing Observational Data Semantics
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Clio: Schema Mapping Creation and Data Exchange
Conceptual Modeling: Foundations and Applications
Building semantic mappings from databases to ontologies
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Detecting and Interpreting Variable Interactions in Observational Ornithology Data
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Database support for enabling data-discovery queries over semantically-annotated observational data
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
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Observational data plays a critical role in many scientific disciplines, and scientists are increasingly interested in performing broad-scale analyses by using data collected as part of many smaller scientific studies. However, while these data sets often contain similar types of information, they are typically represented using very different structures and with little semantic information about the data itself, which creates significant challenges for researchers who wish to discover existing data sets based on data semantics (observation and measurement types) and data content (the values of measurements within a data set). We present a formal framework to address these challenges that consists of a semantic observational model, a high-level semantic annotation language, and a declarative query language that allows researchers to express data-discovery queries over heterogeneous (annotated) data sets. To demonstrate the feasibility of our framework, we also present implementation approaches for efficiently answering discovery queries over semantically annotated data sets.