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Key Takeaways

  • NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning is a research project that addresses the challenge of "incomplete" onto...
  • OWL ontologies provide a formal knowledge representation framework that enables semantic reasoning, and have been widely adopted across domains such as healthcare and bioinformatics.
  • In practice, however, real-world ontologies are often incomplete, which pose challenges for reasoning.
  • This task unifies subsumption verification with ontology abduction, and generalizes the latter by removing the need for a predefined candidate set of missing axioms.
  • We evaluate NeurOWL on real-world ontologies across multiple domains, demonstrating strong and robust performance across different domains.
Paper AbstractExpand

OWL ontologies provide a formal knowledge representation framework that enables semantic reasoning, and have been widely adopted across domains such as healthcare and bioinformatics. In practice, however, real-world ontologies are often incomplete, which pose challenges for reasoning. In this work, we focus on a fundamental subsumption reasoning problem: given an incomplete ontology and a candidate (non-entailed) subsumption, determine whether the subsumption is semantically plausible and, if so, providing a logically sound explanation containing potential missing axioms. This task unifies subsumption verification with ontology abduction, and generalizes the latter by removing the need for a predefined candidate set of missing axioms. To address this subsumption reasoning problem, we propose NeurOWL, an end-to-end neuro-symbolic framework that jointly performs verification and abduction, leveraging both formally defined semantics and textual semantics through Large Language Models and ontology embeddings. We evaluate NeurOWL on real-world ontologies across multiple domains, demonstrating strong and robust performance across different domains.

NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning is a research project that addresses the challenge of "incomplete" ontologies. In fields like healthcare and bioinformatics, ontologies—formal frameworks used to organize knowledge—are often missing certain logical rules (axioms) due to the high cost of manual expert curation. This incompleteness prevents automated systems from correctly identifying relationships between concepts. NeurOWL provides a way to verify if a relationship between two concepts is plausible and, if it is, automatically suggests the missing logical rules needed to make that relationship valid.

Bridging Logic and Language

The framework combines two different ways of processing information: formal logic and human-like language understanding. It uses traditional ontology reasoners to check for existing logical connections and leverages Large Language Models (LLMs) and ontology embeddings to infer missing ones. By integrating these, the system can identify "bridge concepts"—intermediate ideas that connect two concepts—to construct missing axioms. This allows the system to function without needing a predefined list of potential missing rules, making it more flexible than previous methods.

The Three-Stage Reasoning Process

NeurOWL operates through a structured, three-stage pipeline:

  • Logical Checking: The system first uses a standard reasoner to see if the relationship is already supported by the existing ontology.

  • Logical Bridging: If the relationship is not found, the system looks for "bridge" concepts within the existing structure (either by looking at subclasses or superclasses) and uses an LLM to verify if these connections are logically sound.

  • Bridging by Embeddings: If logical structure alone isn't enough, the system uses semantic embeddings to find connections based on the meaning of the concepts. If a bridge is found, it is verified by an LLM; if no bridge exists, the system evaluates the original relationship directly to see if it is a plausible, standalone axiom.

Performance and Results

The researchers tested NeurOWL on real-world datasets, including FoodOn and Snomed CT. The results showed that the framework is highly effective at determining whether a subsumption (a relationship where one concept is a subset of another) is plausible, achieving F1 scores of up to 97. Furthermore, the system demonstrated strong accuracy in generating meaningful explanations for why these relationships should exist, with scores for the correctness of predicted missing axioms reaching up to 89.3.

Key Considerations

A significant advantage of NeurOWL is its ability to handle both positive and negative scenarios, meaning it can identify when a proposed relationship is incorrect rather than just trying to force a justification. The framework is also designed to be adaptable, functioning in both training-free and fine-tuned settings. While it excels at identifying missing axioms, the authors note that the system can be applied iteratively, using its own predicted axioms as a starting point to discover even more detailed and refined knowledge over time.

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