CMOMgen: Complex Multi-Ontology Alignment via Pattern-Guided In-Context Learning

2025-10-24

AI Summary

This work addresses the coarse-grained semantic integration problem in multi-ontology settings by introducing the Complex Multi-Ontology Matching (CMOM) task: mapping each source entity to a logical expression—e.g., conjunction or disjunction—over multiple target entities, enabling fine-grained equivalence modeling and traceable, hierarchical alignment. To this end, we propose CMOMgen, the first end-to-end framework supporting arbitrary numbers of target ontologies and entities. CMOMgen innovatively integrates schema-guided in-context learning with retrieval-augmented generation (RAG): it retrieves semantically relevant ontology classes and reference alignments via analogical retrieval, guiding large language models to generate semantically sound and logically consistent composite mappings. Evaluated on three biomedical datasets, CMOMgen achieves F1 scores of 63%–79%, establishing new state-of-the-art performance on two benchmark tasks. Human evaluation further confirms its generalizability and practicality: 46% of non-reference mappings received the highest rating.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Constructing comprehensive knowledge graphs requires the use of multiple ontologies in order to fully contextualize data into a domain. Ontology matching finds equivalences between concepts interconnecting ontologies and creating a cohesive semantic layer. While the simple pairwise state of the art is well established, simple equivalence mappings cannot provide full semantic integration of related but disjoint ontologies. Complex multi-ontology matching (CMOM) aligns one source entity to composite logical expressions of multiple target entities, establishing more nuanced equivalences and provenance along the ontological hierarchy. We present CMOMgen, the first end-to-end CMOM strategy that generates complete and semantically sound mappings, without establishing any restrictions on the number of target ontologies or entities. Retrieval-Augmented Generation selects relevant classes to compose the mapping and filters matching reference mappings to serve as examples, enhancing In-Context Learning. The strategy was evaluated in three biomedical tasks with partial reference alignments. CMOMgen outperforms baselines in class selection, demonstrating the impact of having a dedicated strategy. Our strategy also achieves a minimum of 63% in F1-score, outperforming all baselines and ablated versions in two out of three tasks and placing second in the third. Furthermore, a manual evaluation of non-reference mappings showed that 46% of the mappings achieve the maximum score, further substantiating its ability to construct semantically sound mappings.

Problem

• Aligns source entities to composite logical expressions
• Enables multi-ontology matching without target restrictions
• Generates semantically sound mappings for knowledge graphs

Innovation

• Pattern-guided in-context learning for ontology alignment
• Retrieval-augmented generation for selecting relevant classes
• Generates complex mappings across multiple target ontologies