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In this paper I aim to explain the approach to the nature and aims of logic in the work of Isaac Watts (1674–1748): Logick: Or, the Right Use of Reason in the Enquiry after Truth (1725). I discuss Watts's notion that the guidance and regulation of the acts and powers of the mind is the proper province of logic, as well as the pedagogical ambitions of his logical works. I focus on the cure of the imagination, which is one member of the more general cure of the intellectual powers that logic under this description seeks. I also provide the historical contexts that are apt to illuminate these features of an early Enlightenment logic: I situate Watts within the early modern development of an approach to logic as a therapeutic art of thinking; and I suggest that the pedagogical nature of Watts's work is indebted to a specific tradition of Protestant practical divinity that fed into the pedagogical practices of early eighteenth-century English religious dissent, which Watts embraced. [ABSTRACT FROM AUTHOR]

IoT devices produce a vast amount of data ranging from personal to sensitive information. Usually, these devices remain connected to the internet so protecting the information produced by them is crucial. Since most of the IoT devices are resource-constrained, they must be supported with lightweight encryption standards to protect information. Recent research has used the concept of context awareness to select the most suitable data encryption standard based on the device resources along with the required information confidentiality level. However, to effectively use the context information, it is required to be organized explicitly while considering the dynamic nature of IoT systems. In this regard, ontology-based systems effectively reduce the volume of manual work while recommending solutions. Currently, these systems cannot work with precision due to multiple uncertain factors of IoT sensory data. To overcome this challenge, this research proposes a fuzzy ontology-based context-aware system to protect IoT device information with the help of an encryption algorithm that considers device capabilities and user priorities regarding the data confidentiality. In order to automate the recommendation process, Semantic Web Rule Language (SWRL) rules and fuzzy logic are used, whereas, Description Logic and RDF Query Language is used to evaluate the results. The evaluation results confirm that the proposed method can produce results according to human perception by significantly increasing the accuracy of prediction. [ABSTRACT FROM AUTHOR]

Weighted knowledge bases for description logics with typicality under a 'concept-wise' multi-preferential semantics provide a logical interpretation of MultiLayer Perceptrons. In this context, Answer Set Programming (ASP) has been shown to be suitable for addressing defeasible reasoning in the finitely many-valued case, providing a |$\varPi ^{p}_{2}$| upper bound on the complexity of the problem, nonetheless leaving unknown the exact complexity and only providing a proof-of-concept implementation. This paper fulfills the lack by providing a |${P^{NP[log]}}$| -completeness result and new ASP encodings that deal with both acyclic and cyclic weighted knowledge bases with large search spaces, as assessed empirically on synthetic test cases. The encodings are used to empower a reasoner for computing solutions and answering queries, possibly interacting with ASP Chef for obtaining an interactive visualization. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This document is a guest editorial from the journal "Enterprise Modelling & Information Systems Architectures" discussing the convergence of enterprise modeling and knowledge graphs. It emphasizes the need to reconnect diagrammatic enterprise modeling and engineering-oriented knowledge structuring. The editorial explores the use of knowledge graphs in enterprise modeling and introduces three papers that showcase different views on how enterprise models and ontologies or semantic graphs can support each other. The document aims to demonstrate the growing research on the convergence between enterprise modeling and knowledge graphs. Additionally, there is a list of references for articles and papers related to enterprise modeling, knowledge graphs, and ontology-driven conceptual modeling. [Extracted from the article]

The internet of things (IoT) has seen rapid expansion, but this growth brings significant privacy challenges due to the large amounts of data generated by myriad IoT devices. To address these challenges, this study introduces Smart Anonymity, a method that determines the optimal data anonymization algorithm for a dataset by assessing its unique features. The solution leverages OWL ontologies grounded in description logic (DL), which facilitates inconsistency checks and the discovery of new facts for data validation. Additionally, machine learning (ML) is incorporated to improve the accuracy of these classifications. ML is also instrumental in recommending suitable anonymization algorithms, with the random forest algorithm being employed explicitly for this purpose. The findings from this research indicate that Smart Anonymity effectively improves user privacy. [ABSTRACT FROM AUTHOR]

This paper provides a set of cut-free complete sequent-style calculi for deontic STIT (‘See To It That’) logics used to formally reason about choice-making, obligations, and norms in a multi-agent setting. We leverage these calculi to write a proof-search algorithm deciding deontic, multi-agent STIT logics with (un)limited choice and introduce a loop-checking mechanism to ensure the termination of the algorithm. Despite the acknowledged potential for deontic reasoning in the context of autonomous, multi-agent scenarios, this work is the first to provide a syntactic decision procedure for this class of logics. Our proofsearch procedure is designed to provide verifiable witnesses/certificates of the (in)validity of formulae, which permits an analysis of the (non)theoremhood of formulae and act as explanations thereof. We show how the proof system and decision algorithm can be used to automate normative reasoning tasks such as duty checking (viz. determining an agent’s obligations relative to a given knowledge base), compliance checking (viz. determining if a choice, considered by an agent as potential conduct, complies with the given knowledge base), and joint fulfillment checking (viz. determining whether under a specified factual context an agent can jointly fulfill all their duties). [ABSTRACT FROM AUTHOR]

Non-Tarskian interpretations of many-valued logics have been widely explored in the logic literature. The development of non-tarskian conceptions of logical consequence set the theoretical foundations for rediscovering well-known (Tarskian) many-valued logics. One may find in distinct authors many novel interpretations of many-valued systems. They are produced through a type of procedure which consists in altering the semantic structure of Tarskian many-valued logics in order to output a non-Tarskian interpretation of these logics. Through this type of transformation the paper explores a uniform way of transforming finitely many-valued Tarskian logics into their non-Tarskian interpretation. Some general properties of carrying out this type of procedure are studied, namely the dualities between these logics and the conditions under which negation-explosive and negation-complete Tarskian logics become non-explosive. [ABSTRACT FROM AUTHOR]

Exposure is a central concept of the health and behavioural sciences needed to study the influence of the environment on the health and behaviour of people within a spatial context. While an increasing number of studies measure different forms of exposure, including the influence of air quality, noise, and crime, the influence of land cover on physical activity, or of the urban environment on food intake, we lack a common conceptual model of environmental exposure that captures its main structure across all this variety. Against the background of such a model, it becomes possible not only to systematically compare different methodological approaches but also to better link and align the content of the vast amount of scientific publications on this topic in a systematic way. For example, an important methodical distinction is between studies that model exposure as an exclusive outcome of some activity versus ones where the environment acts as a direct independent cause (active vs. passive exposure). Here, we propose an information ontology design pattern that can be used to define exposure and to model its variants. It is built around causal relations between concepts including persons, activities, concentrations, exposures, environments and health risks. We formally define environmental stressors and variants of exposure using Description Logic (DL), which allows automatic inference from the RDF-encoded content of a paper. Furthermore, concepts can be linked with data models and modelling methods used in a study. To test the pattern, we translated competency questions into SPARQL queries and ran them over RDF-encoded content. Results show how study characteristics can be classified and summarized in a manner that reflects important methodical differences. [ABSTRACT FROM AUTHOR]

The employment of Machine Learning (ML) techniques in embedded systems has seen constant growth in recent years, especially for black-box ML techniques (such as Artificial Neural Networks (ANNs)). However, despite the successful employment of ML techniques in embedded environments, their performance potential is constrained by the limited computing resources of their embedded computers. Several hardware-based approaches were developed (e.g., using FPGAs and ASICs) to address the constraints of limited computing resources. The scope of this work focuses on improving the performance for Inductive Logic Programming (ILP) on embedded environments. ILP is a powerful logic-based ML technique that uses logic programming to construct human-interpretable ML models, where those logic-based ML models are capable of describing complex and multi-relational concepts. In this work, we present a hardware-based approach that accelerates the hypothesis evaluation task for ILPs in embedded environments that use Description Logic (DL) languages as their logic-based representation. In particular, we target the \(\mathcal {ALCQ}^{\mathcal {(D)}}\) language. According to experimental results (through an FPGA implementation), our presented approach has achieved speedups up to 48.7-fold for a disjunction of 32 concepts on 100 M individuals, where the baseline performance is the sequential CPU performance of the Raspberry Pi 4. For role and concrete role restrictions, the FPGA implementation achieved speedups up to 2.4-fold (for MIN cardinality role restriction on 1M role assertions); all FPGA implemented role and concrete role restrictions have achieved similar speedups. In the worst-case scenario, the FPGA implementation achieved either a similar or slightly better performance than the baseline (for all DL operations); the worst-case scenario resulted from using small datasets such as: using conjunction and disjunction on < 100 individuals, and using role and concrete (float/string) role restrictions on < 100,000 assertions. [ABSTRACT FROM AUTHOR]

We consider the extension of the two-variable guarded fragment logic with local Presburger quantifiers. These are quantifiers that can express properties such as "the number of incoming blue edges plus twice the number of outgoing red edges is at most three times the number of incoming green edges" and captures various description logics with counting, but without constant symbols. We show that the satisfiability problem for this logic is EXP-complete. While the lower bound already holds for the standard two-variable guarded fragment logic, the upper bound is established by a novel, yet simple deterministic graph-based algorithm. [ABSTRACT FROM AUTHOR]

The editorial discusses the importance of Explainable Artificial Intelligence (XAI) in developing trustworthy AI systems, emphasizing the need for explanations to enhance system robustness, prevent bias, and increase user trust. The special issue focuses on the role of ontologies and knowledge in XAI, showcasing research on ontology extensions, explanation ontologies, data journeys, user-centered explanations, and separability in Ontology-based Data Management. The accepted papers demonstrate the utilization of ontologies, knowledge graphs, and knowledge representation in advancing the field of XAI, offering diverse perspectives on achieving explainability in AI systems. [Extracted from the article]

Deep learning models have achieved impressive performance in various tasks, but they are usually opaque with regards to their inner complex operation, obfuscating the reasons for which they make decisions. This opacity raises ethical and legal concerns regarding the real-life use of such models, especially in critical domains such as in medicine, and has led to the emergence of the eXplainable Artificial Intelligence (XAI) field of research, which aims to make the operation of opaque AI systems more comprehensible to humans. The problem of explaining a black-box classifier is often approached by feeding it data and observing its behaviour. In this work, we feed the classifier with data that are part of a knowledge graph, and describe the behaviour with rules that are expressed in the terminology of the knowledge graph, that is understandable by humans. We first theoretically investigate the problem to provide guarantees for the extracted rules and then we investigate the relation of "explanation rules for a specific class" with "semantic queries collecting from the knowledge graph the instances classified by the black-box classifier to this specific class". Thus we approach the problem of extracting explanation rules as a semantic query reverse engineering problem. We develop algorithms for solving this inverse problem as a heuristic search in the space of semantic queries and we evaluate the proposed algorithms on four simulated use-cases and discuss the results. [ABSTRACT FROM AUTHOR]

Background: The exploration of cancer vaccines has yielded a multitude of studies, resulting in a diverse collection of information. The heterogeneity of cancer vaccine data significantly impedes effective integration and analysis. While CanVaxKB serves as a pioneering database for over 670 manually annotated cancer vaccines, it is important to distinguish that a database, on its own, does not offer the structured relationships and standardized definitions found in an ontology. Recognizing this, we expanded the Vaccine Ontology (VO) to include those cancer vaccines present in CanVaxKB that were not initially covered, enhancing VO's capacity to systematically define and interrelate cancer vaccines. Results: An ontology design pattern (ODP) was first developed and applied to semantically represent various cancer vaccines, capturing their associated entities and relations. By applying the ODP, we generated a cancer vaccine template in a tabular format and converted it into the RDF/OWL format for generation of cancer vaccine terms in the VO. '12MP vaccine' was used as an example of cancer vaccines to demonstrate the application of the ODP. VO also reuses reference ontology terms to represent entities such as cancer diseases and vaccine hosts. Description Logic (DL) and SPARQL query scripts were developed and used to query for cancer vaccines based on different vaccine's features and to demonstrate the versatility of the VO representation. Additionally, ontological modeling was applied to illustrate cancer vaccine related concepts and studies for in-depth cancer vaccine analysis. A cancer vaccine-specific VO view, referred to as "CVO," was generated, and it contains 928 classes including 704 cancer vaccines. The CVO OWL file is publicly available on: http://purl.obolibrary.org/obo/vo/cvo.owl, for sharing and applications. Conclusion: To facilitate the standardization, integration, and analysis of cancer vaccine data, we expanded the Vaccine Ontology (VO) to systematically model and represent cancer vaccines. We also developed a pipeline to automate the inclusion of cancer vaccines and associated terms in the VO. This not only enriches the data's standardization and integration, but also leverages ontological modeling to deepen the analysis of cancer vaccine information, maximizing benefits for researchers and clinicians. Availability: The VO-cancer GitHub website is: https://github.com/vaccineontology/VO/tree/master/CVO. [ABSTRACT FROM AUTHOR]

This paper considers a specification rewriting meachanism for a specific fragment of Linear Temporal Logic for Finite traces, DECLAREd, working through an equational logic and rewriting mechanism under customary practitioner assumptions from the Business Process Management literature. By rewriting the specification into an equivalent formula which might be easier to compute, we aim to streamline current state-of-the-art temporal artificial intelligence algorithms working on temporal logic. As this specification rewriting mechanism is ultimately also able to determine with the provided specification is a tautology (always true formula) or a formula containing a temporal contradiction, by detecting the necessity of a specific activity label to be both present and absent within a log, this implies that the proved mechanism is ultimately a SAT-solver for DECLAREd. We prove for the first time, to the best of our knowledge, that this fragment is a polytime fragment of LTLf, while all the previously-investigated fragments or extensions of such a language were in polyspace. We test these considerations over formal synthesis (Lydia), SAT-Solvers (AALTAF) and formal verification (KnoBAB) algorithms, where formal verification can be also run on top of a relational database and can be therefore expressed in terms of relational query answering. We show that all these benefit from the aforementioned assumptions, as running their tasks over a rewritten equivalent specification will improve their running times, thus motivating the pressing need of this approach for practical temporal artificial intelligence scenarios. We validate such claims by testing such algorithms over a Cybersecurity dataset. [ABSTRACT FROM AUTHOR]

The twentieth-century logical mainstream, derived from works by Łukasiewicz and Scholz, pictures the history of logic for the most part as the prehistory of Boolean–Fregean mathematical logic. Particularly, with respect to classical propositional calculus, the Stoic logic has been pictured as an early stage of it and Aristotle's or the Peripatetics' logic as a theory that assumes it. Although it was not emphasised, it follows that the ancient logics contain the principle of explosion. In the endmost quarter of the twentieth century, a competitive view began to spread to the effect that the ancient systems of logic were paraconsistent or relevantistic. In the twenty-first century, the latter view prevails and has every chance of becoming a new orthodoxy. It is claimed that although in Łukasiewicz's argument for the classicality of ancient logics, there are gaps, it may be demonstrated that the ancient logics contain the principle of explosion. [ABSTRACT FROM AUTHOR]

Knowledge graph embeddings represent a group of machine learning techniques which project entities and relations of a knowledge graph to continuous vector spaces. RDF2vec is a scalable embedding approach rooted in the combination of random walks with a language model. It has been successfully used in various applications. Recently, multiple variants to the RDF2vec approach have been proposed, introducing variations both on the walk generation and on the language modeling side. The combination of those different approaches has lead to an increasing family of RDF2vec variants. In this paper, we evaluate a total of twelve RDF2vec variants on a comprehensive set of benchmark models, and compare them to seven existing knowledge graph embedding methods from the family of link prediction approaches. Besides the established GEval benchmark introducing various downstream machine learning tasks on the DBpedia knowledge graph, we also use the new DLCC (Description Logic Class Constructors) benchmark consisting of two gold standards, one based on DBpedia, and one based on synthetically generated graphs. The latter allows for analyzing which ontological patterns in a knowledge graph can actually be learned by different embedding. With this evaluation, we observe that certain tailored RDF2vec variants can lead to improved performance on different downstream tasks, given the nature of the underlying problem, and that they, in particular, have a different behavior in modeling similarity and relatedness. The findings can be used to provide guidance in selecting a particular RDF2vec method for a given task. [ABSTRACT FROM AUTHOR]

Formalisms based on temporal logics interpreted over finite strict linear orders, known in the literature as finite traces, have been used for temporal specification in automated planning, process modelling, (runtime) verification and synthesis of programs, as well as in knowledge representation and reasoning. In this article, we focus on first-order temporal logic on finite traces. We first investigate preservation of equivalences and satisfiability of formulas between finite and infinite traces, by providing a set of semantic and syntactic conditions to guarantee when the distinction between reasoning in the two cases can be blurred. Moreover, we show that the satisfiability problem on finite traces for several decidable fragments of first-order temporal logic is ExpSpace-complete, as in the infinite trace case, while it decreases to NExpTime when finite traces bounded in the number of instants are considered. This leads also to new complexity results for temporal description logics over finite traces. Finally, we investigate applications to planning and verification, in particular by establishing connections with the notions of insensitivity to infiniteness and safety from the literature. [ABSTRACT FROM AUTHOR]

We investigate the impact of non-regular path expressions on the decidability of satisfiability checking and querying in description logics extending ALC. Our primary objects of interest are ALCreg and ALCvpl, the extensions of ALC with path expressions employing, respectively, regular and visibly-pushdown languages. The first one, ALCreg, is a notational variant of the well-known Propositional Dynamic Logic of Fischer and Ladner. The second one, ALCvpl, was introduced and investigated by Löding and Serre in 2007. The logic ALCvpl generalises many known decidable non-regular extensions of ALCreg. We provide a series of undecidability results. First, we show that decidability of the concept satisfiability problem for ALCvpl is lost upon adding the seemingly innocent Self operator. Second, we establish undecidability for the concept satisfiability problem for ALCvpl extended with nominals. Interestingly, our undecidability proof relies only on one single non-regular (visibly-pushdown) language, namely on r#s# := {rnsn | n ∈ N} for fixed role names r and s. Finally, in contrast to the classical database setting, we establish undecidability of query entailment for queries involving non-regular atoms from r #s #, already in the case of ALC-TBoxes. [ABSTRACT FROM AUTHOR]

The modeling of uncertain information is an open problem in ontology research and is a theoretical obstacle to creating a truly semantic web. Currently, ontologies often do not model uncertainty, so stochastic subject matter must either be normalized or rejected entirely. Because uncertainty is omnipresent in the real world, knowledge engineers are often faced with the dilemma of performing prohibitively labor-intensive research or running the risk of rejecting correct information and accepting incorrect information. It would be preferable if ontologies could explicitly model real-world uncertainty and incorporate it into reasoning. We present an ontology framework which is based on a seamless synthesis of description logic and probabilistic semantics. This synthesis is powered by a link between ontology assertions and random variables that allows for automated construction of a probability distribution suitable for inferencing. Furthermore, our approach defines how to represent stochastic, uncertain, or incomplete subject matter. Additionally, this paper describes how to fuse multiple conflicting ontologies into a single knowledge base that can be reasoned with using the methods of both description logic and probabilistic inferencing. This is accomplished by using probabilistic semantics to resolve conflicts between assertions, eliminating the need to delete potentially valid knowledge and perform consistency checks. In our framework, emergent inferences can be made from a fused ontology that were not present in any of the individual ontologies, producing novel insights in a given domain. [ABSTRACT FROM AUTHOR]

Inductive Logic Programming (ILP) is a research field at the intersection between machine learning and logic programming, focusing on developing a formal framework for inductively learning relational descriptions in the form of logic programs from examples and background knowledge. As an emerging method of ILP, Meta-Interpretive Learning (MIL) leverages the specialization of a set of higher-order metarules to learn logic programs. In MIL, the input includes a set of examples, background knowledge, and a set of metarules, while the output is a logic program. MIL executes a depth-first traversal search, where its program search space expands polynomially with the number of predicates in the provided background knowledge and exponentially with the number of clauses in the program, sometimes even leading to search collapse. To address this challenge, this study introduces a strategy that employs the concept of reuse, specifically through the integration of auxiliary predicates, to reduce the number of clauses in programs and improve the learning efficiency. This approach focuses on the proactive identification and reuse of common program patterns. To operationalize this strategy, we introduce MILER, a novel method integrating a predicate generator, program learner, and program evaluator. MILER leverages frequent subgraph mining techniques to detect common patterns from a limited dataset of training samples, subsequently embedding these patterns as auxiliary predicates into the background knowledge. In our experiments involving two Visual Question Answering (VQA) tasks and one program synthesis task, we assessed MILER's approach to utilizing reusable program patterns as auxiliary predicates. The results indicate that, by incorporating these patterns, MILER identifies reusable program patterns, reduces program clauses, and directly decreases the likelihood of timeouts compared to traditional MIL. This leads to improved learning success rates by optimizing computational efforts. [ABSTRACT FROM AUTHOR]

Metamodeling is a general approach to expressing knowledge about classes and properties in an ontology. It is a desirable modeling feature in multiple applications that simplifies the extension and reuse of ontologies. Nevertheless, allowing metamodeling without restrictions is problematic for several reasons, mainly due to undecidability issues. Practical languages, therefore, forbid classes to occur as instances of other classes or treat such occurrences as semantically different objects. Specifically, meta-querying in SPARQL under the Direct Semantic Entailment Regime uses the latter approach, thereby effectively not supporting meta-queries. However, several extensions enabling different metamodeling features have been proposed over the last decade. This paper deals with the Metamodeling Semantics (MS) over OWL 2 QL and the Metamodeling Semantic Entailment Regime (MSER), as proposed in Lenzerini et al. (2015, Description Logics) and Lenzerini et al. (2020, Information Systems 88 , 101294), Cima et al. (2017, Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics , 1–6). A reduction from OWL 2 QL to Datalog for meta-querying was proposed in Cima et al. (2017, Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics , 1–6). In this paper, we experiment with various logic programming tools that support Datalog querying to determine their suitability as back-ends to MSER query answering. These tools stem from different logic programming paradigms (Prolog, pure Datalog, Answer Set Programming, Hybrid Knowledge Bases). Our work shows that the Datalog approach to MSER querying is practical also for sizeable ontologies with limited resources (time and memory). This paper significantly extends Qureshi and Faber (2021, International Joint Conference on Rules and Reasoning , Springer, 218–233.) by a more detailed experimental analysis and more background. [ABSTRACT FROM AUTHOR]

This work investigates the decidability and complexity of database query answering under guarded existential rules with nonmonotonic negation according to the classical stable model semantics. In this setting, existential quantification is interpreted via Skolem functions, and the unique name assumption is adopted. As a first result, we show the decidability of answering first-order queries based on such rules by a translation into the satisfiability problem for guarded second-order formulas having the tree-model property. To obtain precise complexity results for unions of conjunctive queries, we transform the original problem in polynomial time into an intermediate problem that is easier to analyze: query answering for guarded disjunctive existential rules with stratified negation.We obtain precise bounds for the general setting and for various restricted settings. We also consider extensions of the original formalism with negative constraints, keys, and the possibility of negated atoms in queries. Finally, we show how the above results can be used to provide decidability and complexity results for a natural adaptation of the stable model semantics to description logics such as ELHI and the DL-Lite family. [ABSTRACT FROM AUTHOR]

The inclusion of collective intelligence through a semantic focused affective computing can incorporate intelligence to web search and ensure its compliance with the Web 3.0. In this article, a query growth model with inclusive and exclusive ontology semantics has been proposed for diversification of query recommendation in semantic search. The ontology semantics include query augmented ontology generation, agent‐driven attractor‐distractor generation to yield a merged ontology, and endowment of merged ontology by using hybridization of a series of knowledge bases. The strategy further includes the formulation of a semantic network and entity leveraging based on description logics (DLs) to improve the quality of query recommendation. A novel hierarchical entropy cognitive similarity covariance model has been proposed for yielding the most appropriate recommendable query words. The strategy also encompasses the user‐click information for capturing the current user intents to improve the quality queries recommended in semantic search, and thereby incorporate personalization. Experimentations are conducted for the CHiC dataset and the Spring 2006 Query Log dataset and an average accuracy of 96.27% and 92.01%, respectively, with a very low false discovery rate of 0.06 and 0.1 for the respective datasets. [ABSTRACT FROM AUTHOR]

Copyright of Childhood & Philosophy is the property of International Council for Philosophical Inquiry with Children and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article overviews the means for describing and formally analyzing naturallanguage text knowledge under uncertainty. We consider a family of classic attribute languages and logics based on them, their properties, problems, and solution tools. We also give an overview of propositional n-valued logics and fuzzy logics, their syntax, and semantics. Based on the considered logical constructions, we propose syntax and set-theoretic interpretation of n-valued description logic ALCQn that provides means for describing concept intersection, union, complement, value restrictions, and qualitative and quantitative constraints. We consider the means for solving key problems of reasoning over such logics: executability, augmentation, equivalence, and disjunctivity. As an algorithm for calculating executability degree, we consider an extension of the tableau algorithm often used for first-order logic with solving simple numerical constraints. We prove that the algorithm is terminal, complete, and noncontradictory. We also provide several applications for the provided formal representation in natural language processing, including extending results of machine learning models, combining knowledge from multiple sources, and formally describing uncertain facts. [ABSTRACT FROM AUTHOR]

Weighted knowledge bases for description logics with typicality provide a logical interpretation of MultiLayer Perceptrons, based on a "concept-wise" multi-preferential semantics. On the one hand, in the finitely many-valued case, Answer Set Programming (ASP) has been shown to be suitable for addressing defeasible reasoning from weighted knowledge bases for the boolean fragment of ALC. On the other hand, the semantics of weighted knowledge bases with typicality, in their different variants, have suggested some new gradual argumentation semantics, as well as an approach for defeasible reasoning over a weighted argumentation graph, building on the gradual semantics and, specifically on the φ-coherent semantics. In this paper, we explore the relationships between weighted knowledge bases and weighted argumentation graphs, to develop proof methods for defeasible reasoning over an argumentation graph under the φ-coherent semantics, in the finitely-valued case. We establish a mapping from a weighted argumentation graph to a weighted knowledge base as well as a lower bound on the complexity of the problem of verifying graded implications over an argumentation graph in the φ-coherent semantics. We also consider a mapping from weighted knowledge bases to weighted argumentation graphs, and provide an ASP implementation and some experimental results. [ABSTRACT FROM AUTHOR]

The article overviews the means for describing and formally analyzing natural-language text knowledge under uncertainty. We consider a family of classic attribute languages and logics based on them, their properties, problems, and solution tools. We also overview propositional n-valued logics and fuzzy logics, their syntax and semantics. Based on the considered logical constructions, we propose syntax and set-theoretic interpretation of n-valued description logic ALCQn that provides the means for describing concept intersection, union, complement, value restrictions, and qualitative and quantitative constraints. We consider the means for solving key problems of reasoning over such logics: executability, augmentation, equivalence, and disjunctivity. As an algorithm for calculating the executability degree, we consider an extension of the tableau algorithm often used for first-order logic with solving simple numerical constraints. We prove that the algorithm is terminal, complete, and non-contradictory. We also provide several applications for the formal representation in natural language processing, including extending results of machine learning models, combining knowledge from multiple sources and formally describing uncertain facts. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the evolution of prioritized E L ontologies in the presence of new information that can be certain or uncertain. We propose an extension of E L description logic, named E L ⊥ + , within possibility theory to encode such knowledge. This extension provides a natural way to deal with the ordinal scale and represent knowledge in a way that can handle incomplete information and conflicting data. We provided a polynomial algorithm for computing the possibilistic entailment. Then, we defined the evolution process at the semantic and syntactic levels. Interestingly enough, we show that the syntactical algorithm is done in polynomial time. [ABSTRACT FROM AUTHOR]

Conjunctive Query (CQ) answering is a primary reasoning task over knowledge bases. However, when considering expressive description logics, query answering can be computationally very expensive; reasoners for CQ answering, although heavily optimized, often sacrifice expressive power of the input ontology or completeness of the computed answers in order to achieve tractability and scalability for the problem. In this work, we present a hybrid query answering architecture that combines various services to provide a CQ answering service for OWL. Specifically, it combines scalable CQ answering services for tractable languages with a CQ answering service for a more expressive language approaching the full OWL 2. If the query can be fully answered by one of the tractable services, then that service is used, to ensure maximum performance. Otherwise, the tractable services are used to compute lower and upper bound approximations. The union of the lower bounds and the intersection of the upper bounds are then compared. If the bounds do not coincide, then the "gap" answers are checked using the "full" service. These techniques led to the development of two new systems: (i) RSAComb, an efficient implementation of a new tractable answering service for RSA (role safety acyclic) (ii) ACQuA, a reference implementation of the proposed hybrid architecture combining RSAComb, PAGOdA, and HermiT to provide a CQ answering service for OWL. Our extensive evaluation shows how the additional computational cost introduced by reasoning over a more expressive language like RSA can still provide a significant improvement compared to relying on a fully-fledged reasoner. Additionally, we show how ACQuA can reliably match the performance of PAGOdA, a state-of-the-art CQ answering system that uses a similar approach, and can significantly improve performance when PAGOdA extensively relies on the underlying fully-fledged reasoner. [ABSTRACT FROM AUTHOR]

At a commercial gallery in London in 2019 I'm listening to audio recordings of Rose English's 1980s performance Plato's Chair, while a video of the show is projected on the wall ahead. The effect is of the performer describing what she is doing as she does it, the description displaced from one medium or occasion to another. Although, what she appears to be doing – for all her constant motion – is enacting a kind of hiatus while she ponders her next move, alone with herself, removed from her audience, thinking it all through and using words to do so. Description attempts to move things along. At play here, a certain – or uncertain – mimetics. Pleasures, for sure, for those who were there, or are placed there now. And a theatrical know-how – comedy, melodrama, opera-dance, and dressage all get a try-out – which is rooted in repertoire, but which ponders how to proceed. She is at once, she says, a comedian and philosopher. Which is to say also, ironist. In her book-length study of our ordinary acts of self-description, The Words of Selves (2000), Denise Riley locates in irony a 'political astringency' that corrodes 'excessively vaunted' categories, such as the human. But she finds irony also arising spontaneously within injury, compelled into intensities of self-contemplation. The injury, for instance, of one – as human as they come – who describes to me, on the phone, a tree outside her window. 'I don't know what to call it'. Description goes around. [ABSTRACT FROM AUTHOR]

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The Craig interpolation property (CIP) states that an interpolant for an implication exists iff it is valid. The projective Beth definability property (PBDP) states that an explicit definition exists iff a formula stating implicit definability is valid. Thus, the CIP and PBDP reduce potentially hard existence problems to entailment in the underlying logic. Description (and modal) logics with nominals and/or role inclusions do not enjoy the CIP nor the PBDP, but interpolants and explicit definitions have many applications, in particular in concept learning, ontology engineering, and ontology-based datamanagement. In this article, we show that, even without Beth and Craig, the existence of interpolants and explicit definitions is decidable in description logics with nominals and/or role inclusions such as ALCO, ALCH, and ALCHOI and corresponding hybrid modal logics. However, living without Beth and Craig makes these problems harder than entailment: the existence problems become 2ExpTime-complete in the presence of an ontology or the universal modality, and coNExpTime-complete otherwise.We also analyze explicit definition existence if all symbols (except the one that is defined) are admitted in the definition. In this case, the complexity depends on whether one considers individual or concept names. Finally, we consider the problem of computing interpolants and explicit definitions if they exist and turn the complexity upper bound proof into an algorithm computing them, at least for description logics with role inclusions. [ABSTRACT FROM AUTHOR]

Fuzzy spatiotemporal reasoning is extensively used in various application fields such as Geographic Information Systems, Geospatial Artificial Intelligence, and Remote Sensing Systems. However, providing a tractable reasoning mechanism for fuzzy spatiotemporal knowledge is a challenging research problem. Description logics (DLs) are a type of logic-based tractable knowledge representation formalism that allow for describing knowledge structure of an application domain, but they are limited in their ability to express fuzzy spatiotemporal knowledge. To address this limitation, we propose a tractable temporal DL named f- ALC (S) -LTL, which expands linear temporal logic (LTL) by utilizing fuzzy spatial DL f- ALC (S) . In this article, we first define the syntax and formal-semantic model of our logic and investigate a tableau rule-based reasoning procedure to verify satisfiability. We further show the correctness and computational complexity of the reasoning procedure and demonstrate a running example of its application. Finally, we implement a prototype reasoning tool that can determine the satisfiability problem. Our case studies show that our logic f- ALC (S) -LTL is feasible and the prototype reasoning tool actually works. The logic f- ALC (S) -LTL enables tractable reasoning about the dynamic evolution of fuzzy RCC relations over time. [ABSTRACT FROM AUTHOR]

This paper is concerned with knowledge graph embedding with background knowledge, taking the formal perspective of logics. In knowledge graph embedding, knowledge--expressed as a set of triples of the form (a R b) ("a is R-related to b")--is embedded into a real-valued vector space. The embedding helps exploiting geometrical regularities of the space in order to tackle typical inductive tasks of machine learning such as link prediction. Recent embedding approaches also consider incorporating background knowledge, in which the intended meanings of the symbols a, R, b are further constrained via axioms of a theory. Of particular interest are theories expressed in a formal language with a neat semantics and a good balance between expressivity and feasibility. In that case, the knowledge graph together with the background can be considered to be an ontology. This paper develops a cone-based theory for embedding in order to advance the expressivity of the ontology: it works (at least) with ontologies expressed in the description logic ALC, which comprises restricted existential and universal quantifiers, as well as concept negation and concept disjunction. In order to align the classical Tarskian Style semantics for ALC with the sub-symbolic representation of triples, we use the notion of a geometric model of an ALC ontology and show, as one of our main results, that an ALC ontology is satisfiable in the classical sense iff it is satisfiable by a geometric model based on cones. The geometric model, if treated as a partial model, can even be chosen to be faithful, i.e., to reflect all and only the knowledge captured by the ontology. We introduce the class of axis-aligned cones and show that modulo simple geometric operations any distributive logic (such as ALC) interpreted over cones employs this class of cones. Cones are also attractive from a machine learning perspective on knowledge graph embeddings since they give rise to applying conic optimization techniques. [ABSTRACT FROM AUTHOR]

The aim of the Semantic Web is making information and resources from the Web automatically processable by machines. Usually, the uncertainty characterizing much of this information is addressed by means of a probabilistic semantics. Following the vision of a "Probabilistic Semantic Web", a plethora of probabilistic semantics have been proposed: some of them change the syntax and/or the semantics itself of the knowledge representation language, others allow one to annotate axioms of a knowledge base with a probability value. Among the latter, the DISPONTE semantics exploits probabilistic annotations to extend query answering with the capability of returning the probability of a query being true in a domain. In order to promote the adoption of Probabilistic Semantic Web we first developed BUNDLE, a framework that can exploit different underlying (probabilistic and non‐probabilistic) reasoners to perform probabilistic inference under the DISPONTE semantics. In this paper we present a web application for BUNDLE, to show how DISPONTE is easily usable even in already established applications and systems. It allows users to query a DISPONTE knowledge base written or uploaded directly in the application interface by using just a web browser, without the need to install any software on their machine. It is accessible on the web at https://bundle.ml.unife.it/ and also provides some examples for familiarizing with the application. The results of a usability evaluation involving human participants are also reported, showing the relevance and the practical impact of the tool and possible ways for improvement. [ABSTRACT FROM AUTHOR]

In today's time more than 70% of the world's population suffer from eye disnormalities leading to the usage of eyewear or spectacles. Integrating profound technologies with daily utilities could serve some of the issues improving and optimizing our lifestyle to the most. One such way is to infuse nanosized chip in eyewear i.e., powered spectacles or shades to detect the location of the spectacles whenever it is necessary. The nanosized chip proposed has features including self-designed Bluetooth operating digital circuit, timer logic, clock generation using astable multivibrator circuit, emergency button, beep alarm and impact sensor. The values of resistance and capacitace is calculated to be 18 K ohm and 47 uF to obtain 1 Hz frequency. An optimal pin placement arrangement is analyzed, and the timing waveform is simulated using Verilog as proof of logical working of the chip. 13 D flipflops have been calculated to refrain from eye related strains. This paper suggests a bottom-up approach and develops the architectural framework of the chip, its working flow, system on chip top-view, digital logic description of each block and its implementation using Verilog hardware description language (HDL). The complexity and computational cost of the designed chip is minimal thus being commercially viable. [ABSTRACT FROM AUTHOR]

Ontologies can provide a valuable role in the work of cancer registration, particularly as a tool for managing and navigating the various classification systems and coding rules. Further advantages accrue from the ability to formalise the coding rule base using description logics and thereby benefit from the associated automatic reasoning functionality. Drawing from earlier work that showed the viability of applying ontologies in the data validation tasks of cancer registries, an ontology was created using a modular approach to handle the specific checks for childhood cancers. The ontology was able to handle successfully the various inter-variable checks using the axiomatic constructs of the web ontology language. Application of an ontological approach for data validation can greatly simplify the maintenance of the coding rules and facilitate the federation of any centralised validation process to the local level. It also provides an improved means of visualising the rule interdependencies from different perspectives. Performance of the automatic reasoning process can be a limiting issue for very large datasets and will be a focus for future work. Results are provided showing how the ontology is able to validate cancer case records typical for childhood tumours. [ABSTRACT FROM AUTHOR]

Model‐based system autonomy is a complex integration of planning from high‐level goals to low‐level command sequences whose execution controls a system. The need for autonomy has accelerated in recent years to enable complex missions in automotive, space, and defense. During system development, understanding the relationship between system autonomy and the physical environment (including hardware) is critical to supporting trade studies, developing concepts of operations, characterizing risk, and performing testing. This paper describes the initial results of developing Autonomica, an ontology‐based methodology and a framework for autonomous behavior modeling and analysis. This methodology formalizes an architectural pattern for specifying model‐based autonomy as a vocabulary with description logic semantics and provides authoring and analysis capabilities (reasoning, querying, and simulation) for the architectures. The framework implements the methodology in an integrated workbench. A running example of a hypothetical spacecraft mission to a small space body illustrates the ideas. [ABSTRACT FROM AUTHOR]

Manufacturing companies across multiple industries face an increasingly dynamic and unpredictable environment. This development can be seen on both the market and supply side. To respond to these challenges, manufacturing companies must implement smart manufacturing systems and become more flexible and agile. The flexibility in operational planning regarding the scheduling and sequencing of customer orders needs to be increased and new structures must be implemented in manufacturing systems' fundamental design as they constitute much of the operational flexibility available. To this end, smart and more flexible solutions for production planning and control (PPC) are developed. However, scheduling or sequencing is often only considered isolated in a predefined stable environment. Moreover, their orientation on the fundamental logic of the existing IT solutions and their applicability in a dynamic environment is limited. This paper presents a conceptual model for a task-based description logic that can be applied to factory planning, technology planning, and operational control. By using service-oriented architectures, the goal is to generate smart manufacturing systems. The logic is designed to allow for easy and automated maintenance. It is compatible with the existing resource and process allocation logic across operational and strategic factory and production planning. [ABSTRACT FROM AUTHOR]

Background: SNOMED CT is a large terminology system designed to represent all aspects of healthcare. Its current form and content result from decades of bottom-up evolution. Due to SNOMED CT's formal descriptions, it can be considered an ontology. The Basic Formal Ontology (BFO) is a foundational ontology that proposes a small set of disjoint, hierarchically ordered classes, supported by relations and axioms. In contrast, as a typical top-down endeavor, BFO was designed as a foundational framework for domain ontologies in the natural sciences and related disciplines. Whereas it is mostly assumed that domain ontologies should be created as extensions of foundational ontologies, a post-hoc harmonization of consolidated domain ontologies in use, such as SNOMED CT, is known to be challenging. Methods: We explored the feasibility of harmonizing SNOMED CT with BFO, with a focus on the SNOMED CT Clinical Finding hierarchy. With more than 100,000 classes, it accounts for about one third of SNOMED CT's content. In particular, we represented typical SNOMED CT finding/disorder concepts using description logics under BFO. Three representational patterns were created and the logical entailments analyzed. Results: Under a first scrutiny, the clinical intuition that diseases, disorders, signs and symptoms form a homogeneous ontological upper-level class appeared incompatible with BFO's upper-level distinction into continuants and occurrents. The Clinical finding class seemed to be an umbrella for all kinds of entities of clinical interest, such as material entities, processes, states, dispositions, and qualities. This suggests the conclusion that Clinical finding would not be a suitable upper-level class from an BFO perspective. On closer inspection of the taxonomic links within this hierarchy and the implicit meaning derived thereof, it became clear that Clinical finding classes do not characterize the entity (e.g. a fracture, allergy, tumor, pain, hemorrhage, seizure, fever) in a literal sense but rather the condition of a patient having that fracture, allergy, pain etc. This gives sense to the current characteristic of the Clinical Finding hierarchy, in which complex classes are modeled as subclasses of their constituents. Most of these taxonomic links are inferred, as the consequence of the 'role group' design pattern, which is ubiquitous in SNOMED CT and has often been subject of controversy regarding its semantics. Conclusion: Our analyses resulted in the proposal of (i) equating SNOMED CT's 'role group' property with the reflexive and transitive BFO relation 'has occurrent part'; and (ii) reinterpreting Clinical Findings as Clinical Occurrents, i.e. temporally extended entities in an organism, having one or more occurrents as temporal parts that occur in continuants. This re-interpretation was corroborated by a manual analysis of classes under Clinical Finding, as well as the identification of similar modeling patterns in other ontologies. As a result, SNOMED CT does not require any content redesign to establish compatibility with BFO, apart from this re-interpretation, and a suggested re-labeling. Regarding the feasibility of harmonizing terminologies with principled foundational ontologies post-hoc, our results provide support to the assumption that this does not necessarily require major redesign efforts, but rather a careful analysis of the implicit assumptions of terminology curators and users. [ABSTRACT FROM AUTHOR]

In today's time more than 70% of the world's population suffer from eye disnormalities leading to the usage of eyewear or spectacles. Integrating profound technologies with daily utilities could serve some of the issues improving and optimizing our lifestyle to the most. One such way is to infuse nanosized chip in eyewear i.e., powered spectacles or shades to detect the location of the spectacles whenever it is necessary. The nanosized chip proposed has features including self-designed Bluetooth operating digital circuit, timer logic, clock generation using astable multivibrator circuit, emergency button, beep alarm and impact sensor. The values of resistance and capacitace is calculated to be 18 K ohm and 47 uF to obtain 1 Hz frequency. An optimal pin placement arrangement is analyzed, and the timing waveform is simulated using Verilog as proof of logical working of the chip. 13 D flipflops have been calculated to refrain from eye related strains. This paper suggests a bottom-up approach and develops the architectural framework of the chip, its working flow, system on chip top-view, digital logic description of each block and its implementation using Verilog hardware description language (HDL). The complexity and computational cost of the designed chip is minimal thus being commercially viable. [ABSTRACT FROM AUTHOR]

Many scientific fields show great interest in the extraction and processing of spatiotemporal data, such as medicine with an emphasis on epidemiology and neurology, geology, social sciences, meteorology, and a great interest is also observed in the study of transport. Spatiotemporal data differ significantly from spatial data, since spatiotemporal data refer to measurements, which take into account both the place and the time in which they are received, with their respective characteristics, while spatial data refer to and describe information related only to place. The innovation brought about by spatiotemporal data mining has caused a revolution in many scientific fields, and this is because through it we can now provide solutions and answers to complex problems, as well as provide useful and valuable predictions, through predictive learning. However, combining time and place in data mining presents significant challenges and difficulties that must be overcome. Spatiotemporal data mining and analysis is a relatively new approach to data mining which has been studied more systematically in the last decade. The purpose of this article is to provide a good introduction to spatiotemporal data, and through this detailed description, we attempt to introduce descriptive logic and gain a complete knowledge of these data. We aim to introduce a new way of describing them, aiming for future studies, by combining the expressions that arise by type of data, using descriptive logic, with new expressions, that can be derived, to describe future states of objects and environments with great precision, providing accurate predictions. In order to highlight the value of spatiotemporal data, we proceed to give a brief description of ST data in the introduction. We describe the relevant work carried out to date, the types of spatiotemporal (ST) data, their properties and the transformations that can be made between them, attempting, to a small extent, to introduce constraints and rules using descriptive logic, introducing descriptive logic into spatiotemporal data by type, when initially presenting the ST data. The data snapshots by species and similarities between the cases are then described. We describe methods, introducing clustering, dynamic ST clusters, predictive learning, pattern mining frequency, and pattern emergence, and problems such as anomaly detection, identifying time points of changes in the behavior of the observed object, and development of relationships between them. We describe the application of ST data in various fields today, as well as the future work. We finally conclude with our conclusions, with the representation and study of spatiotemporal data can, in combination with other properties which accompany all natural phenomena, through their appropriate processing, lead to safe conclusions regarding the study of problems, and also with great precision in the extraction of predictions by accurately determining future states of an environment or an object. Thus, the importance of ST data makes them particularly valuable today in various scientific fields, and their extraction is a particularly demanding challenge for the future. [ABSTRACT FROM AUTHOR]

In Pietroski (2018) a simple representation language called SMPL is introduced, construed as a hypothesis about core conceptual structure. The present work is a study of this system from a logical perspective. In addition to establishing a completeness result and a complexity characterization for reasoning in the system, we also pinpoint its expressive limits, in particular showing that the fourth corner in the square of opposition ("Some_not") eludes expression. We then study a seemingly small extension, called SMPL+, which allows for a minimal predicate-binding operator. Perhaps surprisingly, the resulting system is shown to encode precisely the concepts expressible in first-order logic. However, unlike the latter class, the class of SMPL+ expressions admits a simple procedural (context-free) characterization. Our contribution brings together research strands in logic—including natural logic, modal logic, description logic, and hybrid logic—with recent advances in semantics and philosophy of language. [ABSTRACT FROM AUTHOR]

Many ΙΤ applications rely on the operationalisation of terminologies, such as multilingual semantic search engines. By operationalisation of terminology, we mean a computational representation of the conceptual system as an ontology of knowledge engineering. This conceptual approach of terminology is close to the ISO principles on terminology as defined by the core standards ISO 1087:2019 and ISO 704:2022. The question then is to study what contribution these standards can make to the construction of ontologies for terminological purposes. In other words, are the terminology principles of ISO soluble in the ontology of knowledge engineering? In this article, we will attempt to provide some answers on the feasibility of implementing ISO principles in description logic, the dominant model in knowledge representation. We will illustrate our proposition with Protégé, the most popular environment for building ontologies. Facing the issues raised by this approach, the article will end with the ontoterminology building environment Tedi directly linked to the ISO principles. [ABSTRACT FROM AUTHOR]

The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people's mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user's clinical information, various medical centers, and users' priority. Then, based on the user's requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology's capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators. [ABSTRACT FROM AUTHOR]

Featured Application: Potential applications of the work include the computerization of production processes and the construction of artificial intelligence-based systems to support and optimize tool selection on existing and newly designed assembly lines in line with Industry 4.0 and Internet of Things paradigms. Fast, accurate, and efficient analysis of production data is a key element of the Industry 4.0 paradigm. This applies not only to newly built solutions but also to the digitalization, automation, and robotization of existing factories and production or repair lines. In particular, technologists' extensive experience and know-how are necessary to design correct technological processes to minimize losses during production and product costs. That is why the proper selection of tools, machine tools, and production parameters during the manufacturing process is so important. Properly developed technology affects the entire production process. This paper presents an attempt to develop a post-hoc model of already existing manufacturing processes with the increased requirements and expectations resulting from the introduction of the Industry 4.0 paradigm. In particular, we relied on fuzzy logic to support the description of uncertainties, incomplete data, and discontinuities in the manufacturing process. This translates into better controls compared to conventional systems. An analysis of the proposed solution's limitations and proposals for further development constitute the novelty and contribution of the article. [ABSTRACT FROM AUTHOR]