Fitting Horn DL Ontologies to ABox and Query Examples: A Tale of Simulation Quantifiers and Finite Models
arXiv:2604.26976v2 Announce Type: replace-cross Abstract: We study the problem of fitting a description logic (DL) ontology to a given set of positive and negative examples that take the form of an ABox and a Boolean query. While previous work has investigated this problem for the expressive DLs ALC and ALCI, we here focus on the Horn DLs EL and ELI, as well as their extensions with the bottom concept. As the query language, we consider atomic queries (AQs), conjunctive queries (CQs), and unions thereof (UCQs). We provide characterization of the existence of a fitting ontology based on simulat
This paper represents continued progress in the theoretical foundations of AI, specifically in formal logic and ontology learning, building on previous work in more expressive description logics.
Improving the ability of AI systems to learn and reason from data using formal knowledge representation is crucial for developing more robust and interpretable AI agents.
This work advances the theoretical understanding of how Horn Description Logics can be 'fit' to examples, potentially enabling more efficient and accurate knowledge acquisition for certain AI applications.
- · AI researchers
- · Developers of knowledge-based AI systems
- · Sectors requiring interpretable AI
- · Systems reliant on purely statistical, black-box AI
The ability to automatically generate or refine ontologies from examples could accelerate knowledge base construction.
More reliable and verifiable knowledge representations could lead to AI systems with stronger guarantees about their behavior.
This could contribute to the development of explainable and trustworthy AI, vital for high-stakes applications like medicine or autonomous systems.
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Read at arXiv cs.AI