Alper unifies entity resolution matching and clustering into an iterative graph refinement and probabilistic label propagation process that adaptively selects LLM queries via a budgeted greedy optimization to outperform cascaded pipelines on eight benchmarks.
PARIS: Probabilistic Alignment of Relations, Instances, and Schema
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
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.
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution
Alper unifies entity resolution matching and clustering into an iterative graph refinement and probabilistic label propagation process that adaptively selects LLM queries via a budgeted greedy optimization to outperform cascaded pipelines on eight benchmarks.