Search Results (55)

This narrative review examines how big data analytics supports geospatial decision-making in smart cities through the combined roles of spatial data foundations, GeoAI methods, and urban digital twins. Methodologically, the article follows a structured narrative and critical review design rather than a PRISMA-based systematic review, bibliometric analysis, or meta-analysis. The paper responds to fragmentation across GIScience, smart-city studies, urban analytics, geospatial data engineering, and digital twin research, where related contributions often remain technically rich but weakly integrated from a decision-oriented perspective. Rather than treating geospatial decision-making as an extension of GIS or as a general expression of data-driven governance, the review frames it as a layered socio-technical process through which heterogeneous urban data are transformed into decision-relevant knowledge. The analysis first clarifies the conceptual evolution from GIS to spatial decision support and urban governance, and then examines the spatial data sources, integration problems, and representational limits that shape smart-city evidence. It also reviews GeoAI and geospatial analytics methods, including spatial statistics, machine learning, spatiotemporal forecasting, graph-based modeling, optimization, and explainable GeoAI. Urban digital twins are then analyzed as decision infrastructures that connect sensing, data integration, synchronization, semantic modeling, simulation, visualization, user interaction, and feedback into planning or operations. The review further maps these capabilities across mobility, land use, utilities, risk management, environmental resilience, public health, and cross-domain decision contexts. Overall, the paper argues that the value of smart-city geoinformation systems depends not on data abundance or model sophistication alone, but on their capacity to support interpretable, accountable, and context-sensitive urban decisions. Full article

Intellectual property reasoning depends on the interaction of factual context, doctrinal tests, exceptions, evidentiary uncertainty, and regulatory objectives. These features make patent, copyright, and trademark analysis difficult to support through text-level processing or isolated rule encoding. This article proposes a bounded DIKWP-guided semantic modeling framework for representing selected intellectual property reasoning patterns as queryable semantic structures. The framework is conceptual and design-oriented; it is specified at the design level through a formal graph characterization of DIKWP, a modular ontology fragment, rule schemas, SPARQL-style queries, and worked examples from patent, copyright, and trademark reasoning. Methodologically, the study uses a qualitative legal-informatics design approach. The three IP domains are selected because they represent complementary reasoning patterns: claim-element correspondence and equivalence screening in patent law, expression and exception analysis in copyright law, and factor-based confusion assessment in trademark law. The examples are used to derive semantic entities, relations, rule-linked structures, uncertainty annotations, explanation paths, and human-review triggers. DIKWP is treated not as a complete legal ontology or autonomous adjudicator, but as a network-structured meta-architecture for coordinating data, information, knowledge, wisdom, and purpose in reviewable legal decision support. The article illustrates how selected IP reasoning patterns can be represented in forms that remain traceable to legal sources and open to human review. It does not claim empirical validation, jurisdiction-specific doctrinal completeness, or autonomous legal decision-making. Its contribution is to specify how semantic legal representation can be made more operational, auditable, and institutionally constrained in the intellectual property domain. Full article

Addressing the challenges of dense conceptual content and intricate knowledge relations in computer science textbooks, where traditional pipeline-based information extraction suffers from error propagation and semantic decoupling, this paper proposes a concept-driven joint extraction method termed CDE (Concept-Driven Extraction).First, the model’s ability to focus on domain-specific terminology is enhanced through conceptual priors and attention re-weighting. This is integrated with a predefined schema and structured instruction templates to achieve normalized output for both entities and relations. Second, efficient domain knowledge transfer for computer science textbooks is realized by performing Low-Rank Adaptation (LoRA) fine-tuning on the Qwen3-4B large language model. Finally, the construction of the computer science textbook knowledge graph is accomplished using the Neo4j graph database. On a self-constructed instruction dataset of computer science textbooks, CDE achieves an F1 score of 81.83%, representing an improvement of approximately 2.47 percentage points over the LKD-KGC baseline. This performance significantly surpasses that of traditional pipeline models and existing joint extraction approaches. Experimental results demonstrate that CDE can effectively improve knowledge extraction accuracy in the textbook domain, thereby providing a novel research avenue for the rapid construction of knowledge graphs for computer science educational materials. Full article

Heritage Science generates vast quantities of heterogeneous data; however, the absence of a shared semantic framework frequently results in fragmented knowledge and compromised reproducibility. This paper introduces CRMhs, an ontology developed as a formal extension of the CIDOC Conceptual Reference Model (CRM), designed to harmonise the documentation of scientific investigations within the cultural heritage domain. By defining specialised classes for scientific activities, study objects and analytical datasets, the model ensures a robust chain of provenance from initial physical sampling to final interpretative outcomes. The efficacy of CRMhs is evidenced in this paper through two archaeological case studies, illustrating how CRMhs enables the integration of diverse analytical data into a coherent and navigable knowledge graph. Broader applications, including the integration of environmental data and its use within Reactive Heritage Digital Twin frameworks, are outlined as ongoing developments. In this way, the model facilitates seamless data interoperability, and it bridges scientific evidence, art-historical and archaeological interpretation, supporting a more integrated approach to the preservation and understanding of cultural heritage. Full article

A large number of ontologies have been developed over the past two decades for the education domain. Some of these ontologies are available in public repositories published within the Linked Open Data cloud. However, a significant portion of educational ontologies remain distributed across project-specific websites, making their discovery and access challenging. Ontologies designed for education often have domain- or task-specific characteristics and conceptual structures. To facilitate their discovery, interoperability, actualization and reuse, it is essential to annotate them with rich, standardized metadata, such as domain coverage, pedagogical objectives, target learner groups, and technical specifications, to enable effective search and support integration within educational systems. Other components, such as knowledge graphs, rules, learning analytics, and machine learning-based models also play an important role. In this research, a conceptual model of a heterogeneous educational ontology repository for storing and reusing ontologies, knowledge graphs, and other objects and tools needed for the development of knowledge bases for intelligent education systems is proposed. An OWL ontology modeling the needed metadata for the description of repository objects and supporting semantic search and recommendations to support the development of knowledge bases for intelligent educational systems is also developed. The proposed heterogeneous ontology repository can help in solving many of the challenges related to hallucinations, transparency, personalization, privacy, and pedagogical alignment that arise when integrating large language models into educational systems by proposing or recommending easy-to-use ontologies for the development of intelligent educational systems, integrating generative AI, symbolic AI, machine learning and statistical techniques. It also integrates LLMs to ensure effective and easy search, recommendation of stored objects, and ontology management. The proposed LLM-powered ontology extraction use case demonstrates an encouraging ontology metadata extraction quality (a precision of about 0.7 and a recall of about 0.9) combined with an ontology development strategy that is easy for education professionals to use. Full article

Background/Objectives: Pediatric obstructive sleep apnea (OSA) is a complex disorder with a variable presentation and often challenging diagnostic testing. The history and physical examination in pediatric OSA frequently differ from those in adults. The treatment options are multifaceted and must be tailored to the individual patient. Artificial intelligence (AI) modalities currently employed in pediatric sleep medicine face several important limitations: modality fragmentation, lack of explainability, and limited semantic integration. Method: Our team proposes a new vision for AI and pediatric sleep medicine. This platform is based on a knowledge graph (KG) framework integrating structured and unstructured data to enable reasoning, personalization, and clinical decision support. Results: This framework represents a conceptual architecture; it has not yet been empirically implemented, and the use cases described herein are illustrative of its intended capabilities. Components of the infrastructure developed for similar applications have been successfully implemented. The quantitative feasibility pilot KG represented 100% multimodal data with >90% semantic completeness. Conclusions: Fully realized and deployed into the clinical space, this pediatric OSA KG system will enhance tertiary care programs and help project tertiary-level pediatric care into underserved regions. Full article

This article introduces the CoLiRa (Computational Literature Review & Analysis) framework, a novel integration of established computational algorithms designed to quantitatively analyze and map the evolution of scientific fields. Employing a human-in-the-loop epistemological approach, CoLiRa combines the scalability of automated algorithms with the semantic coherence of expert-driven qualitative research. The multi-stage pipeline incorporates Latent Dirichlet Allocation (LDA) for thematic discovery, cluster analysis (K-Means and Multidimensional Scaling) for conceptual mapping, and Ordinary Least Squares (OLS) regression to monitor temporal trends. Algorithmic outputs are structurally validated by domain experts using quantitative metrics. The framework’s end-to-end capabilities are demonstrated through a proof-of-concept case study on the semantic enrichment of tabular data, encompassing studies up to 2024 that utilize Semantic Web ontologies, Linked Data, and knowledge graphs. The analysis identifies three core research topics and finds no statistically significant linear trends, suggesting thematic coexistence. This work provides a validated, hybrid computational approach for conducting robust literature reviews and mapping research trajectories. Full article

Spatial and temporal ontologies play a foundational role in modeling dynamic real-world phenomena across domains such as geographic information systems, artificial intelligence, and the Semantic Web. Although decades of research have advanced spatial reasoning, temporal logic, and ontology engineering, fully integrated spatio-temporal frameworks remain fragmented across disciplinary traditions. This paper presents a comprehensive review of spatial, temporal, and spatio-temporal ontologies, examining their conceptual foundations, formal logical models and Semantic Web standards. The literature is analyzed to classify major modeling paradigms and to evaluate their theoretical assumptions, representational capabilities, and computational trade-offs. The review proposes a taxonomy distinguishing foundational ontologies, spatial-centric models, temporal-centric frameworks, integrated spatio-temporal systems. Comparative discussion highlights tensions between logical expressiveness and scalability, as well as challenges related to interoperability and dynamic reasoning. The analysis identifies persistent gaps, including limited native temporal support in description logics, complexity in modeling evolving spatial relations, absence of unified spatio-temporal standards, and lack of standardized evaluation benchmarks. The paper concludes by outlining research directions focused on hybrid ontology–knowledge graph architectures, multi-scale modeling, event-driven semantics, and neuro-symbolic integration. By synthesizing theoretical and applied perspectives, this review provides a structured foundation for advancing interoperable and scalable spatio-temporal knowledge systems capable of supporting next-generation intelligent applications. Full article

The B cell receptor (BCR) repertoire captures an individual’s immunological history and antigen-driven evolution within a vast, high-dimensional sequence space. Although bulk and single-cell adaptive immune receptor repertoire sequencing (AIRR-seq) now enables deep profiling of BCR diversity, interpreting these datasets remains challenging due to strong inter-individual heterogeneity, nonlinear sequence–structure–function relationships, dynamic clonal evolution, and the rarity of functionally relevant clones. Artificial intelligence (AI) provides a conceptual and computational framework for addressing these challenges. Here, we summarize how advanced deep learning architectures, including antibody-specific language models, graph neural networks (GNNs), and generative frameworks, uncover clonal topology, structural features, and antigen-binding semantics. We further highlight applications in cancer, infectious disease, and autoimmunity. Finally, we propose a closed-loop framework that integrates multimodal datasets, interpretable AI, and iterative experimental validation to advance predictive immunology and accelerate therapeutic antibody discovery. Full article

New regulations, such as the EU Deforestation-Free Regulation (EUDR), make verifiable agricultural data (AgData) essential for global trade. However, its value is compromised by a widespread “AgData Paradox”, characterized by distrust and fragmentation. To address this problem, we present AgriTrust, a federated semantic governance framework that automates and governs data sharing. Its key methodological innovation lies in the deep integration of a multi-sectorial governance model with a semantic digital layer, implemented through the AgriTrust Ontology (an OWL ontology for tokenization and traceability) and a multi-vendor, blockchain-agnostic architecture that avoids single-vendor dependence. We demonstrate the framework’s feasibility through simulated case studies in three critical Brazilian supply chains: coffee (EUDR compliance), soybean (mass balance), and beef (animal traceability). Using a semantic reasoning pipeline on a proof-of-concept federated knowledge graph of 2010 triples, we show how AgriTrust enables verifiable provenance representation, automated compliance checking via executable data contracts, and cross-platform asset management. The results provide initial evidence that AgriTrust offers a conceptually coherent blueprint for agricultural data sharing, though operational deployment, scalability testing, and performance validation under real-world conditions remain as future work. Full article

Knowledge Graphs (KGs) are increasingly adopted in cultural heritage research to address challenges of semantic heterogeneity, data fragmentation, and cross-institutional knowledge integration. Despite the rapid growth of KG-based heritage systems, a comprehensive and methodologically rigorous synthesis of existing applications remains limited. To address this gap, this study conducts a ROSES-based systematic review of KG applications in cultural heritage, aiming to examine prevailing application domains, methodological patterns, and emerging research trends. Following the Reporting Standards for Systematic Evidence Syntheses (ROSES), a structured search was conducted in Scopus, Web of Science, and IEEE Xplore. After duplicate removal, screening, eligibility assessment, and quality appraisal, 248 peer-reviewed studies published between 2015 and 2024 were retained for final synthesis. A mixed-method approach combining descriptive analysis and thematic synthesis was employed to analyze KG construction strategies, technological components, application contexts, and reported outcomes. The results indicate that KGs are primarily applied in five interconnected areas: digital recording and preservation, knowledge management and integration, protection and restoration support, cultural transmission and education, and research and innovation. Methodologically, the literature reveals a transition from ontology-driven and manually curated knowledge models toward hybrid approaches integrating artificial intelligence techniques such as natural language processing and machine learning. However, persistent challenges remain, including ontology alignment, scalability, evaluation inconsistency, and limited cross-project interoperability. This review contributes a consolidated and transparent evidence base for KG applications in cultural heritage and advances a conceptual understanding of KGs as socio-technical infrastructures that mediate cultural knowledge representation and interpretation. The findings offer methodological insights and practical implications for researchers, heritage professionals, and system designers, while highlighting directions for future interdisciplinary research. Full article

Ensuring numerical robustness in deep neural networks (DNNs) is critical, as defects like overflow or NaN can cause silent failures. However, automated repair is challenged by fragmented domain knowledge and the semantic gap for general-purpose large language models (LLMs). This work proposes NCKG, a Numerical–Conceptual Knowledge Graph-based method for retrieval-augmented repair of DNN numerical defects. NCKG introduces a unified semantic formalization that explicitly models DNN execution contexts, numerical defects, mitigation methods, and constraint knowledge, transforming dispersed defect knowledge into a consistent, machine-interpretable representation. Based on this formalization, a multi-view semantic graph index is constructed, enabling a hybrid semantic-driven retrieval mechanism that combines structure-aware graph matching with vector similarity. Retrieved, semantically aligned defect–repair knowledge is then used to guide LLMs in generating context-aware repairs. Experimental results demonstrate that NCKG significantly outperforms standard retrieval baselines and consistently improves the quality and correctness of LLM-generated fixes across different model scales. This work demonstrates that explicit semantic structuring and retrieval of domain knowledge are crucial for enabling reliable, automated numerical defect repair in DNNs. Full article

The conservation process is iterative and interactive. Periodic updates stratify data across disciplines and time. Still the transition from raw data to structured knowledge is often slowed by procedural gaps and tooling limitations, creating a semantic divide between abundant digital resources and truly intelligible data. This article proposes a methodological and operational approach for managing the continuity of the information flow within a digitalization process functional to a conservation strategy for the Historical Built Heritage. A graph-structured semantic knowledge base was developed and it is fed by data from heterogeneous sources (Building Information Modeling, reality-based annotation platforms and graph databases), organized according to an explicit conceptual model for representing the building’s diachronic evolution. Interaction and querying are mediated by a prototypical multidimensional visualization environment. The experimentation has proven to anticipate contextualization, to rationalize mapping, to harmonize heterogeneous resources, and to formalize knowledge for sharing and querying. Calabrian heritage, which is part of the region’s identity and subject to natural and anthropogenic risks, is the case of interest. Application scenarios are exemplified in the experiment on San Giovannello, Gerace (RC). Full article

Graphs are used in school, many occupations, and daily life, yet many people struggle to interpret them accurately. To help identify sources of difficulty in graph comprehension, we propose the Pictorial–Semantic–Task Framework. In it, we argue that accurate interpretation of graphs requires integrating pictorial variables (e.g., slope direction, graph format, data points) with semantic variables (e.g., titles, labels, scales, variable types) to determine what the graph represents. Many errors arise because readers fail to coordinate these two sources of information, often basing interpretations solely on pictorial variables. The present theoretical synthesis presents the basic analysis underlying the Pictorial–Semantic–Task Framework and an integrative review of relevant findings from graph encoding, extrapolation, and comparison tasks. The findings show that people encode and recall pictorial information far more accurately than semantic information, and often base interpretations solely on visual patterns even when semantic features call for a different conclusion. Analyses of U.S. textbooks and mass media reveal potential sources of these biased interpretations: systematic imbalances in the types of semantic information provided in textbooks and media seem likely to contribute to biases, emphasizing visual over semantic cues. By describing how perceptual and conceptual processes interact during graph comprehension, we aim to advance theories of cognitive processing in the context of graph comprehension and to derive educational implications for helping children interpret graphs more accurately. Full article

Efficient retrieval of mathematical and structural similarities in System Dynamics models remains a significant challenge for traditional lexical systems, which often fail to capture the contextual dependencies of simulation processes. This paper presents an architectural approach and implementation of a semantic search module integrated into an existing cloud-based modeling and simulation system. The proposed method employs a strategy for serializing graph structures into textual descriptions, followed by the generation of vector embeddings via local ONNX inference and indexing within a vector database (Qdrant). Experimental validation performed on a diverse corpus of complex dynamic models, compares the proposed approach against traditional information retrieval methods (Full-Text Search, Keyword Search in PostgreSQL, and Apache Lucene with Standard and BM25 scoring). The results demonstrate the distinct advantage of semantic search, achieving high precision (over 90%) within the scope of the evaluated corpus and effectively eliminating information noise. In comparison, keyword search exhibited only 24.8% precision with a significant rate of false positives, while standard full-text analysis failed to identify relevant models for complex conceptual queries (0 results). Despite a recorded increase in latency (~2 s), the study proves that the vector-based approach is a significantly more robust solution for detecting hidden semantic connections in mathematical model databases, providing a foundation for future developments toward multi-vector indexing strategies. Full article