AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching
Researchers introduce AgentMap, an LLM-based multi-agent framework designed to simultaneously discover equivalence and subsumption mappings in ontology matching.
- Introduces Hybrid Ontology Matching (HOM) to unify equivalence and subsumption discovery.
- Uses a multi-agent LLM architecture to handle interdependent semantic decisions.
- Overcomes the limitations of traditional single-task ontology matching systems.
Traditional ontology matching (OM) methods typically focus on either equivalence discovery or subsumption matching, but rarely both at once. This limitation prevents a holistic understanding of semantic relationships between different data structures.
The proposed AgentMap framework addresses this by introducing Hybrid Ontology Matching (HOM). It utilizes a multi-agent system where various LLM-driven agents make interdependent semantic decisions to identify complex relationships.
By treating ontology matching as a unified task, AgentMap allows for more sophisticated data integration and semantic interoperability between disparate knowledge bases.
Provides a new framework for automating complex data integration and semantic mapping tasks.
Offers a novel research direction combining multi-agent systems with formal ontology theory.
Improves how different AI systems understand and link structured data.
- Ontology Matching
- The process of finding correspondences between different ontologies to enable data interoperability.
- Subsumption
- A relationship between concepts where one concept is a subset or a more specific version of another.
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