Search Results (470)

Digital twins have become a central paradigm for cultural heritage documentation, monitoring, and preventive preservation. Yet, when cultural heritage systems promise prediction, simulation, intervention planning, and decision support, a more explicit account is needed of the computational commitments behind such claims. This position paper proposes the notion of the heritage world model as a conceptual and architectural abstraction that uses the semantic digital twin as its representational layer and extends it toward prediction, memory, uncertainty-aware reasoning, and intervention evaluation. We define a heritage world model as a structured, temporally updated, semantically grounded, and action-aware model of a heritage asset and its preservation environment, capable of integrating observations, estimating latent risk states, predicting plausible future trajectories, and evaluating interventions under uncertainty. The paper does not present a validated deployed system. Rather, it clarifies the architectural conditions under which a decision-support digital twin infrastructure could support the kind of world-model-like preservation system proposed here. It further argues that such a model becomes operationally meaningful only when it includes a human-supervised controller layer that maps semantic state, predicted risk trajectories, uncertainty, memory, and institutional constraints into preservation-relevant actions, alerts, monitoring adaptations, or requests for expert review. Sensor data, remote sensing, computational models, risk assessments, policies, and conservation actions are interpreted as possible observational, dynamic, and intervention layers of a heritage world model. The paper reviews adjacent work in heritage digital twins, semantic and reactive ontologies, risk-aware preservation, agentic AI, and modern AI world models, and proposes a research agenda for moving toward predictive, memory-bearing, and intervention-aware preservation intelligence. Full article

This article argues that lotus and dharma wheel motifs in the Dunhuang Mogao Caves function not merely as decorative symbols but as active visual apparatuses that generate embodied religious experience through a mechanism we term “totemic mediation.” Drawing on Lévi-Strauss’s structuralist reading of totemism, Descola’s ontological framework, Gell’s theory of art as agency, Meyer’s “sensational form,” and Varela’s neurophenomenology, we define totemic mediation as a triadic mechanism encompassing material–spatial arrangement, ontological transformation of experiential states, and value structure generation. We analyze motifs from Mogao Caves 285, 329, and 361 using a five-step analytic framework: formal–visual description, reconstructed embodied viewing, doctrinal identification, mediation mechanism analysis, and evaluative assessment. The analysis demonstrates that the lotus mediates ontologically along a spatial axis, building a vertical channel between the worldly and the divine through ceiling configurations and upward gazes, while the dharma wheel mediates teleologically across the temporal axis, neutralizing linear temporality through rotational dynamics. Together, these motifs constitute “visual prajñā”—a nonconceptual, embodied cognitive effect that bypasses discursive reasoning to enable direct apprehension of śūnyatā (emptiness). This article offers a replicable analytic framework for examining how religious images operate simultaneously as visual apparatuses and ontological mediators. Full article

Modelling constitutes a disciplined transformation process through which heterogeneous, unstructured evidence is translated into structured representations that support reasoning and decision-making. The integration of generative artificial intelligence into such processes introduces new possibilities for automation, yet risks undermining methodological rigour, traceability, and human accountability. This paper proposes a methodology-grounded multi-agent architecture for constructing structured business ecosystem maps from unstructured document collections. The architecture decomposes the modelling lifecycle into specialised agent functions covering boundary specification, source discovery, document analysis, semantic extraction, and controlled model editing, addressing four of the five methodology stages while leaving automated completeness verification outside the current scope. A central orchestrator coordinates agents while enforcing ontological constraints derived from a formal modelling methodology. All proposed modifications are staged for human review before execution, and each map element maintains explicit provenance links to source material. To evaluate the reliability and correctness of generative modelling pipelines, a hybrid evaluation framework integrates operational metrics, semantic assessment using an LLM-based judge, and human agreement validation. Empirical evaluation across 34 generative models and 4382 experimental runs characterises capabilities across modelling tasks. In a controlled single-document extraction task, text-based extraction achieves a mean semantic match score of 0.947, whereas interaction extraction scores 0.431 and visual diagram interpretation scores 0.470, identifying relational reasoning and multimodal interpretation as principal bottlenecks. Model performance varies across agent roles, with task-aligned model selection associated with larger performance changes than hyperparameter tuning; the architecture’s causal contribution is not isolated, and comparison against monolithic or ablated baselines remains future work. Full article

The paper presents a proposed clinical intelligence system, which is called NLP-Bayesian Optimization Clinical Model (NBO-CM), to study large-scale unstructured electronic health record narratives in the MIMIC-IV discharge and radiology note datasets with the help of a structured pipeline of clinical text preprocessing, feature extraction and probabilistic modeling to solve the linguistic variability, missing information and uncertainty in clinical decisions. Text preprocessing methods are first applied in the standardization of clinical narratives, such as text tokenization, text normalization, text lemmatization, text segmentation, and text negation detection. Next, the unstructured text is converted into structured clinical variables with the help of feature extraction techniques, including named entity recognition, medical concept normalization using UMLS/SNOMED ontologies, generating contextual embeddings, and vectorizing text using the Term Frequency–Inverse Document Frequency (TF-IDF) technique. Bayesian Networks are then used to model these features, with dependency structure learning based on the Poor-and-Rich Optimization algorithm (PRO), having the ability to explore probabilistic relationships efficiently, and parameter estimation based on expectation–maximization, providing robust learning with incomplete and uncertain conditions of data. Lastly, probabilistic reasoning and inference are used to predict diseases, prioritize risks and make clinical inferences with clear measures of uncertainty and interpretability. Experimental analysis of real-world MIMIC-IV clinical notes indicates that the framework is more effective in terms of diagnostic accuracy, predictive strength, and clinical explainability than traditional machine learning methods, resulting in a scaled and explainable framework of intelligent clinical decision support systems in complex care settings. Full article

Patent infringement detection requires coordinated interpretation of technical claims, legal standards, and contextual evidence. This study proposes a semantic AI framework for patent infringement detection grounded in the DIKWP network and artificial consciousness theory. The DIKWP network organizes the analytical modules as interacting semantic spaces rather than as a strictly layered pipeline. This design supports iterative semantic interpretation, knowledge integration, and purpose-oriented reasoning. The framework integrates document ingestion, semantic information extraction, ontology-based knowledge representation, rule-guided inference, and decision support. The system processes patent claims, product descriptions, and prior-art documents with patent-oriented NLP. Named entity recognition and subject–action–object parsing convert unstructured text into structured semantic representations. Legal and technical ontologies support claim-element interpretation. Knowledge graphs, semantic pattern matching, and inference rules then align claim elements with product features and identify potential infringement risks. A prototype implementation demonstrates end-to-end processing from raw text to infringement-oriented assessment. The evaluation was conducted in two layers. First, a controlled synthetic patent–product corpus was used to isolate claim-element reasoning, rule-guided inference, and purpose-conditioned operating modes. Second, a real-world pilot corpus was constructed from publicly available patent claims and real product technical descriptions, including manufacturer manuals, technical datasheets, official product webpages, installation guides, and technical brochures. The controlled-corpus results show that the DIKWP network improves over keyword-matching and ontology-only baselines by integrating semantic coverage, claim-level legal reasoning, and explainable output. The real-world pilot provides a preliminary external-validity check of whether the framework can preserve element-level reasoning under realistic drafting styles, domain terminology, incomplete product evidence, and borderline claim-to-product correspondences. These findings provide preliminary evidence of feasibility and analytical value, rather than a final benchmark of litigation-level performance. Full article

Mental health interventions involve complex and evolving situations that require careful reasoning and transparency. This paper presents a knowledge-based system designed to simulate and analyze intervention strategies for student depression in a controlled and explainable setting. The system combines reinforcement learning with formal ontological modeling. A simulation environment, grounded in large-scale integrated student mental health datasets containing questionnaire-derived indicators, represents the evolution of students’ psychological states under a set of clinically informed intervention actions. The proposed framework is evaluated using a composite dataset constructed by integrating multiple publicly available student mental health datasets from Kaggle and Figshare, incorporating integrated student mental health datasets containing questionnaire-derived indicator measures such as depression, anxiety, and lifestyle indicators. A learning agent explores alternative intervention strategies through interaction with this environment. All states, actions, and outcomes are formalized within an OWL ontology, making the decision structure explicit. By embedding learned policies into a structured knowledge representation, the system allows intervention dynamics to be inspected, queried, and analyzed independently of the underlying learning mechanism. Reinforcement learning is used to generate and refine candidate strategies, while ontology provides a stable and interpretable model of the decision space. Experimental results show that the approach can identify coherent intervention strategies within the simulation environment while preserving transparency. The study demonstrates how adaptive learning and symbolic knowledge representation can be integrated within a single knowledge-based system, offering a structured and explainable approach to sequential decision analysis in sensitive domains. Full article

Smart cities generate vast, heterogeneous data streams from transportation networks, energy grids, environmental sensors, and public services, yet the semantic fragmentation of these data silos prevents urban operators from deriving actionable, cross-domain intelligence. Knowledge graphs (KGs) have emerged as a powerful paradigm for integrating diverse, large-scale data collections through graph-based representations of entities and their relationships. This paper applies the Design Science Research Methodology (DSRM) to design, develop, and evaluate UrbanKG, a layered artifact that deploys knowledge graphs as the semantic backbone of smart city data infrastructure. We demonstrate the framework through a proof-of-concept implementation using publicly available urban datasets across five domains, yielding a 287,000-triple knowledge graph validated through cross-domain SPARQL queries and accessibility analysis. Following the six DSRM process steps—problem identification, objective definition, design and development, demonstration, evaluation, and communication—the framework addresses ontology design, multi-source data fusion, federated governance, temporal reasoning, and hybrid deductive–inductive inference. The artifact satisfies all five design objectives and contributes four transferable design principles. Six open research challenges are identified as the forward research agenda. Full article

This paper presents a process-centric ontology for the semantic representation of cyber–physical systems (CPSs) within knowledge-based production planning (KPP). The approach integrates physical systems (PSs), cyber systems (CSs), and CPSs into a unified semantic model based on a three-layer classification. The ontology was implemented using OWL and integrated into a Neo4j-based graph architecture to support semantic querying and process modeling. The evaluation was conducted using prototypical manufacturing scenarios, including semiconductor and mechanical engineering domains. Validation included (i) consistency checking using the HermiT reasoner, (ii) execution of SPARQL queries for retrieving CPS-related process information, and (iii) integration into a three-stage planning model. The results show that the ontology enables consistent semantic representation and cross-domain querying of CPS-based production processes. The work provides a validated proof-of-concept and establishes a foundation for future research on ontology-based production systems. Full article

In a global business environment marked by digital disruption, Small and Medium-sized Enterprises (SMEs) must integrate digital transformation with strategic agility and organizational resilience. This study addresses the fragmentation of the current management literature by developing an AI-driven meta-theory through a high-performance computational synthesis of 4811 academic publications from the OpenAlex database. Utilizing a theoretically grounded hybrid framework of lexical filtering (TF-IDF), semantic embedding (SciBERT), and a diverse ensemble of five Large Language Models (LLMs), we move beyond descriptive mapping to identify the ontological and integrative mechanisms of SME adaptation. The methodology is validated through a multi-stage expert audit of model reasoning traces to ensure theoretical alignment. Results reveal a clear dominance of Contingency Theory (20.5%) and Resource-Based View (14.1%), which are re-conceptualized here as Regulatory–Technical Brokerage and Internal Fortification. Through Social Network Analysis (SNA) and Aggregate Constraint metrics, the study identifies Innovation Frontiers that are operationally challenging to synthesize through traditional manual reviews at this scale. The research concludes by formulating four meta-theoretical propositions and an integrative synergetic mechanism, explaining how SME resilience emerges as an emergent property of cross-layer alignment between technical, cognitive, and structural logics. By providing this causal roadmap, the study establishes a robust, AI-augmented blueprint for SMEs to function as intelligent, self-regulating nodes within a Post-Normal digital ecosystem. Full article

The advancement of autonomous robotic systems has led to significant capabilities in perception, localization, mapping, and control, yet a critical challenge remains in representing and preserving relational semantics, contextual reasoning, and cognitive transparency essential for collaboration in dynamic, human-centric environments. This paper introduces a unified architecture comprising the Ontology Neural Network (ONN) and the Ontological Real-Time Semantic Fabric (ORTSF) to address this challenge. The ONN formalizes relational semantic reasoning as a dynamic topological process by embedding Forman–Ricci curvature, persistent homology, and semantic tensor structures within a unified loss formulation, aiming to maintain relational integrity as scenes evolve. Building upon ONN, the ORTSF transforms reasoning traces into actionable control commands while compensating for system delays through predictive operators designed to preserve phase margins. Theoretical analysis and extensive simulations demonstrate that ORTSF maintains designed phase margins, offering advantages over classical delay compensation methods. Empirical studies indicate the framework’s effectiveness in unifying semantic cognition and robust control, providing a mathematically principled solution for cognitive robotics. Full article

Autonomous driving systems require the integration of heterogeneous sensor data, distributed V2X communication, and safety-critical decision-making into coherent and interpretable world models. This review provides a systematic analysis of knowledge graph (KG)-based approaches in autonomous driving between 2015 and 2025, following a PRISMA-aligned methodology. The literature is organised along a perception → representation → reasoning → decision taxonomy, covering traffic ontologies, V2X knowledge integration, dynamic KG updates, real-time reasoning architectures, and benchmark datasets. A clear shift from static representational ontologies toward predictive and, in a smaller subset, closed-loop validated neuro-symbolic architectures. Knowledge graphs emerge as semantic integration layers that improve contextual reasoning, explainability, and rule compliance in safety-critical environments. Key challenges include scalable real-time reasoning, standardised evaluation frameworks, and safety-aligned integration of learning-based components. Full article

This article examines the 1910 philosophical dispute between Vladimir Ern and Semyon Frank in post-1905 Russia as a dispute over the criterion of philosophy itself. The controversy arose in a field where the meaning of “Russian philosophy,” the authority of neo-Kantian nauchnost’ [scientificity], the religious-ontological program of Put’, and the problem of culture had become closely interconnected. The article argues that the central issue concerned what makes a claim philosophical: participation in an antecedent order of being, or conceptual articulation, proof, and universally valid justification. Ern’s intervention is presented as an attempt to reconstitute philosophy through Logos. For Ern, modern rationalism separates the discursive-logical from the “fullness of reason,” producing ratio as an autonomous and ultimately meonic form of thought; Logos, by contrast, names the ontological principle through which thought remains inwardly bound to being. Frank’s response locates the issue in the concept of philosophy itself. While acknowledging intuition, ontologism, and the insufficiency of one-sided rationalism, he insists that every appeal to being becomes philosophical only when it enters the medium of concepts, reasons, and proof. The article argues that the controversy turns on two irreducible conditions internal to philosophy itself: thought must remain faithful to being, yet it must do so in a form through which its claims become philosophically valid. Read in this way, the Ern–Frank exchange discloses a constitutive tension between ontology and conceptual justification, and between historical embodiment and universal validity. Full article

Large language models (LLMs) are an emerging artificial intelligence-driven technology, based on transformer architecture. LLMs are widely used in modern education, both by learners and tutors, as standalone tools or integrated into e-learning systems, where they can support personalization, adaptive learning, automated assessment and feedback, content generation, and intelligent tutoring. LLMs offer many benefits for learners, but they also have significant limitations. One approach to address the limitations of LLMs is to combine them with other intelligent technologies. The primary goal of this systematic survey is to identify appropriate supporting technologies, mechanisms of use, and methodological approaches able to help overcome the limitations of LLMs and support their responsible and effective use in education. For this reason, analysis and discussion of recent scientific research (published over the last four years) accessible through Google Scholar, ACM, IEEE Xplore, or indexed in Scopus or Web of Science (WoS) is performed. A bibliometric analysis of results from the initial general query strings is used to refine and formulate more specific search queries during the literature retrieval process in the selected databases. Full-text exploration of relevant search results serves as a source for critical analysis and deductions leading to the following conclusion: LLMs should be integrated into e-learning systems, combined with knowledge graphs, ontologies, learning analytics, and multimodal reasoning to enhance reliability, improve pedagogical effectiveness, and enable true personalization. New pedagogical approaches are also needed to ensure the effective use of LLMs in both tutoring and assessment contexts. Therefore, the authors propose methodological guidelines for integrating LLMs in complex modular educational systems. Full article

The article introduces the Hylomorphism Inventory (HI), a new instrument designed to measure lay beliefs about the soul–body relationship that are congruent with the Aristotelian–Thomistic framework of hylomorphism. Although research on intuitive ontology has predominantly focused on dualist and monist models, the hylomorphic perspective—central to Catholic anthropology yet difficult to articulate in everyday cognition—remains largely unexplored. Drawing on research in intuitive anthropology, we conceptualize hylomorphic beliefs as endorsing the human person as a psychophysical unity in which the soul functions as the organizing form of the body. Using a theory-driven approach and expert evaluation, we developed an initial 10-item scale and tested it in a nationwide online sample of Polish adults (n = 407). Exploratory (EFA) and confirmatory factor analyses (CFA), supported by nonparametric Mokken scaling, converged on a primarily unidimensional 9-item solution with high internal consistency (α = 0.89, ordinal α = 0.91, ω ≈ 0.90). Validity analyses revealed that HI scores were strongly associated with beliefs emphasizing the integration of body, mind, and soul, but only weakly related to their mere endorsement as components. This pattern suggests that what distinguishes hylomorphism at the psychological level is not belief in the soul per se, but belief in the unity of the human person. The HI provides a parsimonious tool for differentiating lay anthropological models and enables empirical investigation of how hylomorphism-congruent beliefs relate to moral reasoning, spiritual practices, and broader psychological functioning. Full article

As cleaner production gains prominence in wooden product manufacturing, green evaluation of process schemes during early design is crucial. However, dust concentration, a key environmental indicator in wood product manufacturing, is often evaluated in a subjective and fragmented manner, which greatly hinders the selection of green process schemes in early design. To address this gap, an adaptability evaluation model for green process schemes was proposed based on process knowledge graphs (PKG) and fuzzy Bayesian network (FBN), with the objective of minimizing dust concentration. First, a PKG for wooden products was constructed based on the requirement-function-structure-characteristic-process-equipment (RFSCPE) ontology using patents and process manuals. Second, candidate process schemes were generated via the PKG, and dust-related causal relationships encoded in the PKG were mapped onto a Bayesian network structure. Third, conditional probabilities were obtained by combining probabilistic hesitant fuzzy sets and experimental dust data. The FBN was then updated to perform probabilistic reasoning on dust concentration. Finally, a case study on a wooden toy car validated the proposed approach, and sensitivity analysis identified the key dust-influencing factors, thereby providing quantitative support for greener process decisions. Full article