This paper presents a versatile framework for evaluating fuzzy quantification queries over both standard and fuzzy ontologies as well as knowledge graphs. The primary objective is the retrieval of individuals that satisfy queries articulated via Type I or Type II fuzzy quantified expressions. A key advantage of the proposed approach is its inherent adaptability: it remains entirely agnostic to the quantifier type, the underlying evaluation method, and the specific data source of the ontology (i.e., OWL ontologies or RDFS knowledge graphs). Furthermore, we present Q2S2, a publicly accessible implementation of this system developed to support future research.

Automatising the process of verifying whether the design of a building conforms to a certain regulation is an important task to save time and money. SmartNorms4BIM is a novel building regulatory compliance-checking system that facilitates the automatic evaluation of Building Information Modelling (BIM) models. The system's architecture follows Eastman's four stages: translation of norms into a machine-readable format, BIM model preparation, execution of the verification process, and report generation. Our proposal uses Semantic Web technologies (OWL2 ontologies and semantic reasoners) to represent the knowledge and detect violations of the norms automatically, Large Language Models (LLMs) to interpret and transform building norms into OWL 2 ontologies, fuzzy logic to manage vagueness in the norms, and specific algorithms to reason with geometric knowledge. A Java desktop prototype was developed to determine whether a BIM model complies with a given set of norms. To validate our approach, two real-world use cases were studied.

Post-traumatic stress disorder (PTSD) is a heterogeneous neuropsychiatric condition that develops after exposure to traumatic events. Its diagnosis is currently governed by two major international classification systems, Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases) ICD, which differ in structure, granularity, and diagnostic thresholds. Moreover, PTSD assessment is also multimodal, spanning structured interviews, self-report psychometric instruments, and increasing neurophysiological recordings such as electroencephalography (EEG). This study presents OnTEPT, an OWL-based multimodal ontology designed to formalize DSM-5 and ICD-11 diagnostic criteria, integrate psychometric instruments and EEG-derived features, and support automated reasoning over individual patient instances. OnTEPT was developed following Ontology Development 101 (OD101) in Protégé 5. A hybrid strategy was used, combining top-down and bottom-up approaches and reusing concepts from health and mental health ontologies. The current version includes 112 classes, 18 object properties, and 113 data properties. DSM-5 (A–H) and ICD-11 (1–6) criteria are formalized as OWL equivalence axioms. These use constraints on cardinality, duration, clinical significance, and exclusions. Dissociative and complex PTSD are also represented. Four instruments are modeled at the item-level with cutoff thresholds, and two of them are linked to diagnostic criteria through annotations. The ontology was populated with psychometric data from 304 patients, EEG data from 41 participants, and 30 structured synthetic instances generated with Owlready2. 36 quantitative EEG features are encoded as typed data properties. Validation with HermiT, Pellet, and FaCT + + confirmed logical consistency and class satisfiability. Four intentionally inconsistent instances were correctly rejected due to classification issues. Query-based validation retrieved 113 patients above screening thresholds. It also found three with full DSM-5 profiles, twelve under ICD-11, two with dissociative PTSD, and one with complex PTSD. OnTEPT provides a computable semantic framework that harmonizes PTSD diagnosis and its subtypes between DSM-5 and ICD-11. It integrates psychometric and EEG information within a single ontology. Its main contribution is an inferential layer that enables the automated classification of patient instances using both diagnostic systems. The architecture is extensible and ready to include biomarker-based rules, longitudinal data, and semantic workflows to support clinical decision-making.

In many real-world domains, knowledge is inherently vague or imprecise - features that classical ontology languages, based on crisp Description Logics (DLs), are unable to capture. This shortcoming poses particular challenges for applications in the Semantic Web and Explainable Artificial Intelligence (XAI), where robust reasoning over graded information is essential. Fuzzy ontologies address this limitation by enriching DLs with fuzzy logic, enabling the expression of partial truth and supporting more nuanced modelling of real-world knowledge. We present fuzzy-dl-owl2, a complete re-engineering in Python of the fuzzyDL reasoner and the Fuzzy OWL 2 framework. The former is an expressive fuzzy DL reasoner, while the latter allows for defining fuzzy ontologies within OWL 2. Our contribution addresses several shortcomings of the original software, including semantic inconsistencies, rigid architectural design, and limited solver integration. The re-implementation features a modular class hierarchy tailored for extensibility, supports a broader range of Mixed-Integer Linear Programming (MILP) solvers (including open-source alternatives), and corrects IRI ambiguities arising from overlapping ontological elements. Furthermore, a dedicated Python library (pyowl2) has also been developed to handle OWL 2 annotations in a standards-compliant manner, improving interoperability with existing Semantic Web tooling and resolving IRI ambiguities. The resulting framework offers a portable, extensible, and theoretically grounded platform for reasoning with fuzzy ontologies, suitable for both research and deployment in vague-aware systems. Performance tests have also been conducted that show improved execution times w.r.t. the original Java implementation. The source code and full documentation are publicly available to facilitate community adoption and further development.

In the field of knowledge management and computer science, ontologies provide a structured framework for modeling domain-specific knowledge by defining concepts and their relationships. However, the lack of tools that provide effective visualization is still a significant challenge. While numerous ontology editors and viewers exist, most of them fail to graphically represent ontology structures in a meaningful and non-overwhelming way, limiting users' ability to comprehend dependencies and properties within large ontological frameworks. In this paper, we present OntView, an ontology viewer that is designed to provide users with an intuitive visual representation of ontology concepts and their formal definitions through a user-friendly interface. Building on the use of a DL reasoner, OntView follows a "What you see is what you meant" paradigm, showing the actual inferred knowledge. One key aspect for this is its ability to visualize General Concept Inclusions (GCI), a feature absent in existing visualization tools. Moreover, to avoid a possible information overload, OntView also offers different ways to show a simplified view of the ontology by: 1) creating ontology summaries by assessing the importance of the concepts (according to different available algorithms), 2) focusing the visualization on the existing TBox elements between two given classes and 3) allowing to hide/show different branches in a dynamic way without losing the semantics. OntView has been released with an open-source license for the whole community.

The need to deal with uncertain semantics is rising in importance in most of the important technology trends, and consequently, many proposals have emerged as solutions in recent years. Fuzzy ontologies were proposed to remedy the limitations of standard ontologies using fuzzy logic to deal with vague and imprecise knowledge. Nevertheless, fuzzy ontologies cannot deal with probabilistic knowledge which is an important characteristic of most real-world applications. This paper proposes a novel solution that aims at enhancing the knowledge representation and reasoning in fuzzy ontologies. Indeed, the proposed solution is a probabilistic extension of fuzzy ontologies with Fuzzy Bayesian Networks (FBN) that we named Probabilistic Fuzzy Ontologies (ProbFuzzOnto). It takes into account vague, imprecise, and probabilistic knowledge simultaneously. Moreover, this paper proposes a process to guide ontology engineers step by step in building ProbFuzzOnto. Also, it provides reasoning algorithms to drive implicit knowledge by utilizing explicit knowledge stored in a fuzzy ontology based on fuzzy Bayesian inference. To show the usefulness of the proposed solution, a case study in Renal Cancer is presented.

Representing knowledge with ontologies and performing reasoning with semantic reasoners is important in many intelligent applications. However, existing reasoners do not take into account the available resources of the device where they run, which can be important in many scenarios such as reasoning with very large ontologies or reasoning on resource-constrained mobile devices. In this paper, we propose a novel approach to adapt the size of knowledge managed by applications, taking into account several criteria about resources available (such as time, memory, and battery consumption), at the same time. Thus, rather than giving no answer due to the lack of resources needed to deal with a full ontology, we propose a novel architecture to compute a subontology to provide an incomplete answer at least. Our approach makes use of existing approaches to predict the performance of semantic reasoners and to compute ontology modularisation and ontology partition, but taking into account the associated resource consumption. We also propose a novel measure to estimate the semantic loss when replacing the original ontology by a subontology. Finally, we present an implementation and evaluation of the whole pipeline, showing that the semantic loss incurred in the process is acceptable.

PurposeThere is an increasing interest in the use of knowledge graphs to represent real-world knowledge and a common need to manage imprecise knowledge in many real-world applications. This paper aims to study approaches to solve flexible queries over knowledge graphs.Design/methodology/approachBy introducing fuzzy logic in the query answering process, the authors are able to obtain a novel algorithm to solve flexible queries over knowledge graphs. This approach is implemented in the FUzzy Knowledge Graphs system, a software tool with an intuitive user-graphical interface.FindingsThis approach makes it possible to reuse semantic web standards (RDF, SPARQL and OWL 2) and builds a fuzzy layer on top of them. The application to a use case shows that the system can aggregate information in different ways by selecting different fusion operators and adapting to different user needs.Originality/valueThis approach is more general than similar previous works in the literature and provides a specific way to represent the flexible restrictions (using fuzzy OWL 2 datatypes).

Semantic Building Information Modeling (BIM) consists in translating data expressed using BIM formats (namely IFC) into Semantic Web files using RDF serializations (e.g., Turtle). This enables the inference of new knowledge and constraint checking, among other advantages. While several software tools for translating BIM models into Semantic Web languages have been proposed in the literature, they differ in the features exposed.This paper analyzes and empirically compares some of these tools (namely, IFC converters translating an input IFC model into an RDF graph), identifying their strengths and main limitations. Our methodology includes measuring computation times of common tasks (file conversion, query and inference over output files), assessing the retention of knowledge (particularly, geometric information) and examining reasoning capabilities (complexity and completeness of the resulting models). Our results show that IFCtoLBD is the best option in many cases. IFCtoRDF and IFC2LD are slower but better preserve geometric information, while KGG is faster at the expense of losing information in the translation.

Semantic Query Answering consists of retrieving individuals from a Knowledge Base, usually an OWL 2 ontology or an RDF knowledge graph, that satisfy a Semantic Query expressed via query terms. This paper proposes a novel approach to improve Semantic Query Answering in cases where individuals have missing values with respect to the query terms occurring in a Semantic Query. In our approach, the retrieved instances satisfy some query terms, but not necessarily all. Our general approach, based on the use of fuzzy aggregation operators, is complemented with concrete strategies to evaluate such Semantic Queries, together with an implemented prototype.

The Left Recursive Form of the Convex Combination (CONV–LRF) is a linguistic aggregation operator which has been recently proposed in the framework of fuzzy ontology learning. The objective of this paper is to study some properties of the operator, namely commutativity, smoothness, internality, monotonicity, and orness. Our results show that, contrary to what preliminary experiments suggested, CONV–LRF has a similar orness that the Right Recursive Form (CONV–RRF).

The OntoLex-Lemon model provides a vocabulary to enrich ontologies with linguistic information that can be exploited by Natural Language Processing applications. The increasing uptake of Lemon illustrates the growing interest in combining linguistic information and Semantic Web technologies. In this paper, we present Fuzzy Lemon, an extension of Lemon that allows to assign an uncertainty degree to lexical semantic relations. Our approach is based on an OWL ontology that defines a hierarchy of data properties encoding different types of uncertainty. We also illustrate the usefulness of Fuzzy Lemon by showing that it can be used to represent the confidence degrees of automatically discovered translations between pairs of bilingual dictionaries from the Apertium family.

The increasing interest in Knowledge Graphs to represent real-world knowledge and the common need to manage imprecise knowledge in many real-world applications demand the study of approaches to solve flexible queries over Knowledge Graphs. In this paper, we propose a novel approach to solve that problem which reuses Semantic Web standards (RDF and SPARQL) and builds a fuzzy layer on top of them.