Modern ontologies undergo continuous evolution to accommodate new domain knowledge, correct modeling errors, and adapt to changing user requirements. Monitoring these changes is crucial for maintaining ontology quality and understanding the semantic impact of modifications on dependent systems and applications. This paper describes OntoLDiff, a highly efficient system for tracking the logical difference between two ontologies formulated in the description logic ELH. Intuitively, the logical difference between two versions of an ontology refers to the set of axioms entailed by one version but not the other, indicating the information gain or loss between them. Typically, such axioms, referred to as 'witnesses ', can be infinite, making logical difference computation infeasible. To address this challenge, OntoLDiff employs a Uniform Interpolation (UI) approach to compute a finite representation of these axioms. Instead of computing all the entailments of one ontology but not the other, which would be computationally infeasible, the UI-based approach focuses on identifying only the strongest entailments, from which all witnesses can in principle be computed from its deductive closure. Despite UI's computational complexity, OntoLDiff is currently the only tool that efficiently tracks logical differences in industrial-scale ontologies, enabling ontology curators to precisely identify meaningful changes during ontology evolution.

Ontologies are being utilized widely as sources for formally organized information in a range of fields. The SNOMED CT ontology is a key resource in national and international health sectors for automatically linking information captured by diverse clinical information systems and research data ensuring consistent patient data capture and effective data analytics and decision support. Offering a comprehensive multilingual vocabulary for encoding clinical knowledge of multiple domains, the ontology is large and new releases are created regularly to reflect domain changes and user requirements. The main contribution of the paper is a novel automated approach for tracking semantic differences of subdomains in different versions of SNOMED CT targeted at terminologists, debuggers, ontology evaluators and developers of software using SNOMED CT. Whereas the semantic difference sets produced with existing methods are rather large and difficult to analyze, our method produces concise semantic difference sets for user-specified input focus concepts. Our method is based on subontology generation and semantic difference computation using uniform interpolation, which aids in finding inferred differences that other semantic difference tools do not reveal. The obtained semantic difference sets are related to the meaning of focus concept definitions for specific ontology subdomains, where some of these differences would not have been generated without this focused method for computing semantic differences between ontologies. A case study using SNOMED CT has shown the proposed approach is useful for domain experts.

This paper is concerned with the problem of computing the semantic difference between different versions of large-scale ontological knowledge bases using a uniform interpolation (UI) approach. The semantic difference between two versions of an ontology are the axioms entailed by one version but not the other version, reflecting the evolutionary changes of the content of the ontology. In general, computing such axioms is not computationally feasible, since there are infinitely many of them. UI is an advanced reasoning technique that seeks to create restricted views of ontologies; it provides an effective means for computing a finite representation of the difference between two ontologies. While existing UI methods are designed for languages that are either more expressive or less expressive than the description logic ELH, the underlying language of typical large-scale ontologies, in this paper, we introduce a practical UI method tailored for the task of computing the semantic difference in large-scale ELH-ontologies. The method is terminating, sound, and can always compute UI results possibly including fresh definer symbols. Two case studies on different versions of the SNOMED CT terminology show that the method has overcome major limitations of existing UI methods and can be used to reveal modeling changes that have occurred over successive releases of SNOMED CT.

In this paper, we present a method for extracting subontologies from $\mathcalELH $ ontologies for a set of symbols. The approach is focused on the generation of upwardly abstracted definitions of concepts, which is a technique for computing definitions expressed using closest primitive ancestors. The subontologies returned by the method are evaluated for quality and compared to extracts computed with locality-based modularisation and uniform interpolation. Our subontology generation method produces promising results in terms of size and relevance to the needs of domain experts.

We are interested in the computation of ontology extracts based on forgetting from large ontologies in real-world scenarios. Such scenarios require nearly all of the terms in the ontology to be forgotten, which poses a significant challenge to forgetting tools. In this paper we show that modularization and forgetting can be combined beneficially in order to compute ontology extracts. While a module is a subset of axioms of a given ontology, the solution of forgetting (also known as a uniform interpolant) is a compact representation of the ontology limited to a subset of the signature. The approach introduced in this paper uses an iterative workflow of four stages: (i)~extension of the given signature and, if needed partitioning, (ii)~modularization, (iii)~forgetting, and (iv)~evaluation by domain expert. For modularization we use three kinds of modules: locality-based, semantic and minimal subsumption modules. For forgetting three tools are used: NUI, LETHE and FAME. An evaluation on the SNOMED CT and NCIt ontologies for standard concept name lists showed that precomputing ontology modules reduces the number of terms that need to be forgotten. An advantage of the presented approach is high precision of the computed ontology extracts.