Functional Dependencies (FDs) define attribute relationships based on syntactic equality, and, when used in data cleaning, they erroneously label syntactically different but semantically equivalent values as errors. We enhance dependency-based data cleaning with Ontology Functional Dependencies (OFDs), which express semantic attribute relationships such as synonyms and is-a hierarchies defined by an ontology. Our technical contributions are twofold: 1) theoretical foundations for OFDs, including a set of sound and complete axioms and a linear-time inference procedure, and 2) an algorithm for discovering OFDs (exact ones and ones that hold with some exceptions) from data that uses the axioms to prune the exponential search space in the number of attributes. We demonstrate the efficiency of our techniques on real datasets, and we show that OFDs can significantly reduce the number of false positive errors in data cleaning techniques that rely on traditional FDs.
Ontology functional dependencies (OFD) on relational data have been proposed to capture synonyms and is-a relationships defined in an ontology REF .
Efficient Discovery of Ontology Functional Dependencies
CIKM '17
Computer Science