PARIS: Probabilistic Alignment of Relations, Instances, and Schema

TL;DR

PARIS is a probabilistic system for automatically aligning ontologies by matching instances, relations, and classes, improving integration of diverse Semantic Web ontologies without parameter tuning.

Contribution

It introduces a holistic, probabilistic approach to ontology alignment that simultaneously aligns instances, relations, and classes, enhancing accuracy and efficiency.

Findings

Achieves around 90% precision in large ontology experiments

Operates without parameter tuning due to probabilistic framework

Demonstrates efficiency and high accuracy in extensive tests

Abstract

One of the main challenges that the Semantic Web faces is the integration of a growing number of independently designed ontologies. In this work, we present PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic, i.e., we measure degrees of matchings based on probability estimates. This allows PARIS to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with some of the world's largest ontologies.