Efficient Discovery of Ontology Functional Dependencies

TL;DR

This paper introduces Ontology Functional Dependencies (OFDs), extending traditional data constraints with ontology-based relationships, and provides theoretical foundations, discovery algorithms, and experimental validation for improved data quality management.

Contribution

It defines and formalizes OFDs, develops sound and complete axioms, and presents scalable algorithms for their discovery, enhancing data cleaning with ontology-aware constraints.

Findings

Algorithms efficiently discover OFDs in large datasets.

OFDs improve data cleaning accuracy by capturing broader relationships.

Experimental results demonstrate scalability and effectiveness of the proposed methods.

Abstract

Poor data quality has become a pervasive issue due to the increasing complexity and size of modern datasets. Constraint based data cleaning techniques rely on integrity constraints as a benchmark to identify and correct errors. Data values that do not satisfy the given set of constraints are flagged as dirty, and data updates are made to re-align the data and the constraints. However, many errors often require user input to resolve due to domain expertise defining specific terminology and relationships. For example, in pharmaceuticals, 'Advil' \emph{is-a} brand name for 'ibuprofen' that can be captured in a pharmaceutical ontology. While functional dependencies (FDs) have traditionally been used in existing data cleaning solutions to model syntactic equivalence, they are not able to model broader relationships (e.g., is-a) defined by an ontology. In this paper, we take a first step towards extending the set of data quality constraints used in data cleaning by defining and discovering \emph{Ontology Functional Dependencies} (OFDs). We lay out theoretical and practical foundations for OFDs, including a set of sound and complete axioms, and a linear inference procedure. We then develop effective algorithms for discovering OFDs, and a set of optimizations that efficiently prune the search space. Our experimental evaluation using real data show the scalability and accuracy of our algorithms.