Current neural biomedical entity linking (BioEL) models treat ontologies as flat dictionaries, ignoring the rich terminological knowledge (TBox) that defines concept boundaries. Consequently, they struggle with contextual ambiguity, often retrieving logically inconsistent candidates based solely on surface similarity. We present OntoEL, a neuro-symbolic framework that shifts BioEL from surface-level matching to logic-grounded reasoning. OntoEL integrates differentiable fuzzy EL⊥ reasoning into the retrieval pipeline as a consistency-aware re-ranker, employing a hybrid strategy: structural TBox reasoning is delegated to classical polynomial-time reasoners, while the sigmoidal Reichenbach implication performs soft type-consistency evaluation, effectively resolving the "implication bias" gradient pathology in previous neuro-symbolic methods. By enforcing ontological axioms as differentiable soft constraints, OntoEL aligns neural representations with logical truth. Comprehensive experiments on three benchmarks (MedMentions, BC5CDR, and NCBI Disease) demonstrate state-of-the-art performance, surpassing strong baselines by up to 4.2% in Accuracy@1. On highly ambiguous mentions requiring ontological reasoning, our method corrects 71.2% of retrieval errors, proving the efficacy of incorporating logical semantics into neural retrieval.

Developing ontologies for the Semantic Web is a time-consuming and error-prone task that typically requires the investment of considerable manpower and resources, as well as collaborative efforts. A potentially better idea is to reuse the “off-the-shelf” ontologies, whenever possible, somehow as per certain demands and requirements. A promising way to achieve ontology reuse is through creating views of ontologies, analogous to creating views of databases, with the resulting views focusing on specific topics and content of the original ontologies. This paper explores the problem of creating views of ontologies using a uniform interpolation approach. In particular, we develop a novel and practical uniform interpolation method for creating signature-based views for ontologies specified in the description logic , a very expressive description logic for which uniform interpolation has not been fully addressed. The method is terminating and sound, and computes uniform interpolants of -ontologies by eliminating from the input ontologies the names not used in the view using a forgetting procedure. This makes it the first and so far the only approach to eliminate both concept and (non-transitive) role names from -ontologies. Despite the inherent difficulty of uniform interpolation for this level of expressivity, an empirical evaluation with a prototypical implementation show very good success rates on a corpus of real-world ontologies, and demonstrates clear algorithmic advantage over the state-of-the-art system LETHE. This is extremely useful from the semantic web perspective, as it provides knowledge engineers with a powerful tool to create views of ontologies for ontology reuse.

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.

This paper tackles the problem of computing views of OWL ontologies using a forgetting-based approach. In traditional relational databases, a view is a subset of a database, whereas in ontologies, a view is more than a subset; it contains not only axioms contained in the original ontology, but may also contain newly-derived axioms entailed by the original ontology (implicitly contained in the original ontology). Specifically, given an ontology , the signature of is the set of all the names in , and a view of is a new ontology obtained from using only part of ’s signature, namely the target signature, while preserving all logical entailments up to the target signature. Computing views of OWL ontologies is useful for Semantic Web applications such as ontology-based query answering, in a way that the view can be used as a substitute of the original ontology to answer queries formulated with the target signature, and information hiding, in the sense that it restricts users from viewing certain information of an ontology.

This paper describes a Java-based forgetting system UI-FAME for creating views of ontologies. In relational databases, a view is a subset of the database, while in ontologies, a view is more than a subset; it contains not only axioms that are contained in the original ontology, but also newly-derived axioms that are entailed by the original ontology. Forgetting is a form of non-standard reasoning that can be used to create views of ontologies by eliminating from the ontologies a set of concept and role names, namely the forgetting signature, while keeping all logical consequences over the names in the remaining signature. We compared UI-FAME with publicly accessible forgetting systems, namely LETHE and an early prototypical version of UI-FAME, over the 'AL'-TBox fragment of 494 ontologies taken from the Oxford Ontology Library. The results showed that UI-FAME had better success rates than LETHE and its prototypical version, and outsped LETHE by a large margin. UI-FAME has been integrated as back-end support in Babylon Health's (a leading digital healthcare company based in London) knowledge base (formed by the merger and alignment of medical ontologies from different sources) interfaces for their main concerns of ontology analysis and keeping track of the changes in different versions of its knowledge base.