Search Results (19)

Despite the widespread adoption of BIM, information exchange across disciplines remains hindered by heterogeneous structures at the tabular data level, particularly when integrating data across multiple discipline-specific models. Manual mapping, rigid templates, or one-off programming scripts are labor-intensive and difficult to scale, limiting automated querying, cross-model aggregation, and schedule-level analytics. This study proposes a lightweight, workflow-driven approach for semantic normalization and cross-model integration of BIM schedule data, with optional script-supported workflow configuration used only to assist the configuration of deterministic, rule-guided mapping logic, rather than serving as a core analytical method. By introducing a customizable subcategory layer, the workflow enables fine-grained semantic alignment and efficient normalization across diverse schedule datasets, implemented through lightweight Python scripting and rule-guided semantic matching used solely as a supporting mechanism for deterministic field mapping. Using structural, architectural, and HVAC models, we demonstrate a stepwise process including data cleaning, hierarchical classification, consistency checking, batch analytics, and automated computation of cross-model metrics such as opening-to-wall ratios. Sample-based validation confirms the workflow’s reliability, achieving semantic mapping agreement rates above 95% and reducing manual processing time by more than 85%. The workflow is readily extensible to other disciplines and modeling conventions, supporting high-throughput data integration for tasks such as design coordination, semantic alignment, RFI reduction, accelerated design reviews, and data-driven decision making. Overall, rather than introducing a new algorithm, the contribution of this work lies in formalizing a reusable, schedule-level workflow abstraction that enables consistent semantic alignment and automated cross-model aggregation without relying on rigid ontologies or training-intensive learning-based models. Any optional tooling used during workflow configuration is auxiliary and does not constitute a standalone learning-based method requiring model training or performance benchmarking. This provides a reusable methodological foundation for scalable, schedule-level BIM data integration and cross-model analytics. Full article

Background: Chest X-ray (CXR) is widely used for the assessment of thoracic diseases, yet automated multi-label interpretation remains challenging due to subtle visual patterns, overlapping anatomical structures, and frequent co-occurrence of abnormalities. While recent deep learning models have shown strong performance, limitations in interpretability, anatomical awareness, and robustness continue to hinder their clinical adoption. Methods: The proposed framework employs a hybrid ConvNeXtV2–Vision Transformer (ViT) architecture that combines convolutional feature extraction for capturing fine-grained local patterns with transformer-based global reasoning to model long-range contextual dependencies. The model is trained exclusively using image-level annotations. In addition to classification, three complementary post hoc components are integrated to enhance model trust and interpretability. A segmentation-aware Gradient-weighted class activation mapping (Grad-CAM) module leverages CheXmask lung and heart segmentations to highlight anatomically relevant regions and quantify predictive evidence inside and outside the lungs. An ontology-driven neuro-symbolic reasoning layer translates Grad-CAM activations into structured, rule-based explanations aligned with clinical concepts such as “basal effusion” and “enlarged cardiac silhouette”. Furthermore, a lightweight out-of-distribution (OOD) detection module based on confidence scores, energy scores, and Mahalanobis distance scores is employed to identify inputs that deviate from the training distribution. Results: On the VinBigData test set, the model achieved a macro-AUROC of 0.9525 and a Micro AUROC of 0.9777 when trained solely with image-level annotations. External evaluation further demonstrated strong generalisation, yielding macro-AUROC scores of 0.9106 on NIH ChestXray14 and 0.8487 on CheXpert (frontal views). Both Grad-CAM visualisations and ontology-based reasoning remained coherent on unseen data, while the OOD module successfully flagged non-thoracic images. Conclusions: Overall, the proposed approach demonstrates that hybrid convolutional neural network (CNN)–vision transformer (ViT) architectures, combined with anatomy-aware explainability and symbolic reasoning, can support automated chest X-ray diagnosis in a manner that is accurate, transparent, and safety-aware. Full article

This article presents an industry-ready ontology for the machine learning domain, which is named “ML Ontology”. While based on lightweight modelling languages, ML ontology provides novel features including built-in queries and quality assurance, as well as sophisticated reasoning. With ca. 700 individuals that define key ML concepts and ca. 5000 RDF triples, ML Ontology ranks among the largest domain-specific ontologies for ML. An experiment to estimate the correctness and completeness of ML terminology included in ML Ontology indicates an F1-score of 0.83. A benchmark evaluating query performance reveals query response times far below 100 ms even for complex queries and memory consumption below 3.5 MB. Its industry-readiness is demonstrated by benchmarks as well as two use case implementations within a data science platform. ML Ontology is open source and published under an MIT license. Full article

Reality-capture has made point clouds a primary spatial data source, yet processing and integration limits hinder their potential. Prior reviews focus on isolated phases; by contrast, Smart Point Clouds (SPCs)—augmenting points with semantics, relations, and query interfaces to enable reasoning—received limited attention. This systematic review synthesizes the state-of-the-art SPC terminology and methods to propose a modular pipeline. Following PRISMA, we searched Scopus, Web of Science, and Google Scholar up to June 2025. We included English-language studies in geomatics and engineering presenting novel SPC methods. Fifty-eight publications met eligibility criteria: Direct (n = 22), Indirect (n = 22), and New Use (n = 14). We formalize an operative SPC definition—queryable, ontology-linked, provenance-aware—and map contributions across traditional point cloud processing stages (from acquisition to modeling). Evidence shows practical value in cultural heritage, urban planning, and AEC/FM via semantic queries, rule checks, and auditable updates. Comparative qualitative analysis reveals cross-study trends: higher and more uniform density stabilizes features but increases computation, and hybrid neuro-symbolic classification improves long-tail consistency; however, methodological heterogeneity precluded quantitative synthesis. We distill a configurable eight-module pipeline and identify open challenges in data at scale, domain transfer, temporal (4D) updates, surface exports, query usability, and sensor fusion. Finally, we recommend lightweight reporting standards to improve discoverability and reuse. Full article

Recent advances in large language models (LLMs) have driven substantial progress in knowledge base question answering (KBQA), particularly under few-shot settings. However, symbolic program generation remains challenging due to its strict structural constraints and high sensitivity to generation errors. Existing few-shot methods often rely on multi-turn strategies, such as rule-based step-by-step reasoning or iterative self-correction, which introduce additional latency and exacerbate error propagation. We present CBR², a case-based reasoning framework with dual retrieval guidance for single-pass symbolic program generation. Instead of generating programs interactively, CBR² constructs a unified structure-aware prompt that integrates two complementary types of retrieval: (1) structured knowledge from ontologies and factual triples, and (2) reasoning exemplars retrieved via semantic and function-level similarity. A lightweight similarity model is trained to retrieve structurally aligned programs, enabling effective transfer of abstract reasoning patterns. Experiments on KQA Pro and MetaQA demonstrate that CBR² achieves significant improvements in both accuracy and syntactic robustness. Specifically on KQA Pro, it boosts Hits@1 from 72.70% to 82.13% and reduces syntax errors by 25%, surpassing the previous few-shot state-of-the-art. Full article

This paper introduces the concept of evolving actor–network ontologies (EANO) as a new paradigm for cultural digital twins. Building on actor–network theory, EANO reframes ontologies from static representations into reflexive, dynamic structures in which semantic interpretations are continuously negotiated among heterogeneous actors. We propose a five-layer architecture that operationalizes this principle, embedding reflexivity, actor salience, and systemic parameters such as resistance and volatility directly into the ontological model. To illustrate this approach, we present minimal simulations that demonstrate how different actor constellations and systemic conditions lead to distinct patterns of semantic evolution, ranging from expert erosion to contested equilibria and balanced coexistence. Rather than serving as predictive models, these simulations exemplify how EANO captures semantic plurality and contestation within a transparent and interpretable framework. The contribution of this work is thus twofold: it provides a conceptual foundation for evolving ontologies in digital heritage and a lightweight demonstration of how such models can be instantiated and explored computationally. Full article

Recent developments in next-generation sequencing technology have led to the creation of extensive, open-source protein databases consisting of hundreds of millions of sequences. To render these sequences applicable in biomedical applications, they must be meticulously annotated by wet lab testing or extracting them from existing literature. Over the last few years, researchers have developed numerous automatic annotation systems, particularly deep learning models based on machine learning and artificial intelligence, to address this issue. In this work, we propose a transformer-based fusion model capable of predicting Gene Ontology (GO) terms from full-scale protein sequences, achieving state-of-the-art accuracy compared to other contemporary machine learning annotation systems. The approach performs particularly well on clustered split datasets, which comprise training and testing samples originating from distinct distributions that are structurally diverse. This demonstrates that the model is able to understand both short and long term dependencies within the protein’s structure and can capture sequence features that are predictive of the various GO terms. Furthermore, the technique is lightweight and less computationally expensive compared to the benchmark methods, while at the same time unaffected by sequence length, rendering it appropriate for diverse applications with varying sequence lengths. Full article

Mineral prospecting from vast geological text corpora is impeded by challenges in domain-specific semantic interpretation and knowledge synthesis. General-purpose Large Language Models (LLMs) struggle to parse the complex lexicon and relational semantics of geological texts, limiting their utility for constructing precise knowledge graphs (KGs). Our novel framework addresses this gap by integrating a domain-specific LLM, GeoGPT, with a lightweight retrieval-augmented generation architecture, LightRAG. Within this framework, GeoGPT automates the construction of a high-quality mineral-prospecting KG by performing ontology definition, entity recognition, and relation extraction. The LightRAG component then leverages this KG to power a specialized geological question-answering (Q&A) system featuring a dual-layer retrieval mechanism for enhanced precision and an incremental update capability for dynamic knowledge incorporation. The results indicate that the proposed method achieves a mean F1-score of 0.835 for entity extraction, representing a 17% to 25% performance improvement over general-purpose large models using generic prompts. Furthermore, the geological Q&A model, built upon the LightRAG framework with GeoGPT as its core, demonstrates a superior win rate against the DeepSeek-V3 and Qwen2.5-72B general-purpose large models by 8–29% in the geochemistry domain and 53–78% in the remote sensing geology domain. This study establishes an effective and scalable methodology for intelligent geological text analysis, enabling lightweight, high-performance Q&A systems that accelerate knowledge discovery in mineral exploration. Full article

The rapid growth in scientific knowledge has created a critical need for advanced systems capable of managing mathematical knowledge at scale. This study presents a novel approach that integrates ontology-based knowledge representation with large language models (LLMs) to automate the extraction, organization, and reasoning of mathematical knowledge from LaTeX documents. The proposed system enhances Mathematical Knowledge Management (MKM) by enabling structured storage, semantic querying, and logical validation of mathematical statements. The key innovations include a lightweight ontology for modeling hypotheses, conclusions, and proofs, and algorithms for optimizing assumptions and generating pseudo-demonstrations. A user-friendly web interface supports visualization and interaction with the knowledge graph, facilitating tasks such as curriculum validation and intelligent tutoring. The results demonstrate high accuracy in mathematical statement extraction and ontology population, with potential scalability for handling large datasets. This work bridges the gap between symbolic knowledge and data-driven reasoning, offering a robust solution for scalable, interpretable, and precise MKM. Full article

In Building Information Modeling (BIM)-driven collaboration, the workflow for information management utilizes a Common Data Environment (CDE). The core idea of a CDE is to serve as a single source of truth, enabling efficient coordination among diverse stakeholders. Nevertheless, investigations into employing CDEs to manage projects reveal that procuring commercial CDE solutions is too expensive and functionally redundant for small and medium-sized enterprises (SMEs) and small research organizations, and there is a lack of experience in using CDE tools. Consequently, this study aimed to provide a cheap and lightweight alternative. It proposes a three-layered CDE framework: decentralized databases enabling work in distinct software environments; resource description framework (RDF)-based metadata facilitating seamless data communication; and microservices enabling data collection and reorganization via standardized APIs and query languages. We also apply the CDE framework to structural life cycle assessment (LCA). The results show that a lightweight CDE solution is achievable using tools like the bcfOWL ontology, RESTful APIs, and ASP.NET 6 Clean architecture. This paper offers a scalable framework that reduces infrastructure complexity while allowing users the freedom to integrate diverse tools and APIs for customized information management workflows. This paper’s CDE architecture surpasses traditional commercial software in terms of its flexibility and scalability, facilitating broader CDE applications in the construction industry. Full article

The representation and utilization of environmental information by service robots has become increasingly challenging. In order to solve the problems that the service robot platform has, such as high timeliness requirements for indoor environment recognition tasks and the small scale of indoor scene data, a method and model for rapid classification of household environment domain knowledge is proposed, which can achieve high recognition accuracy by using a small-scale indoor scene and tool dataset. This paper uses a knowledge graph to associate data for home service robots. The application requirements of knowledge graphs for home service robots are analyzed to establish a rule base for the system. A domain ontology of the home environment is constructed for use in the knowledge graph system, and the interior functional areas and functional tools are classified. This designed knowledge graph contributes to the state of the art by improving the accuracy and efficiency of service decision making. The lightweight network MobileNetV3 is used to pre-train the model, and a lightweight convolution method with good feature extraction performance is selected. This proposal adopts a combination of MobileNetV3 and transfer learning, integrating large-scale pre-training with fine-tuning for the home environment to address the challenge of limited data for home robots. The results show that the proposed model achieves higher recognition accuracy and recognition speed than other common methods, meeting the work requirements of service robots. With the Scene15 dataset, the proposed scheme has the highest recognition accuracy of 0.8815 and the fastest recognition speed of 63.11 microseconds per sheet. Full article

Schematic knowledge, an important component of knowledge graphs (KGs), defines a rich set of logical axioms based on concepts and relations to support knowledge integration, reasoning, and heterogeneity elimination over KGs. Although several KGs consist of lots of factual knowledge, their schematic knowledge (e.g., $subclassOf$ axioms, $disjointWith$ axioms) is far from complete. Currently, existing KG embedding methods for completing schematic knowledge still suffer from two limitations. Firstly, existing embedding methods designed to encode factual knowledge pay little attention to the completion of schematic knowledge (e.g., axioms). Secondly, several methods try to preserve logical properties of relations for completing schematic knowledge, but they cannot simultaneously preserve the transitivity (e.g., $subclassOf$) and symmetry (e.g., $disjointWith$) of axioms well. To solve these issues, we propose a cosine-based embedding method named CosE tailored for completing lightweight schematic knowledge in DL-Lite${}_{core}$. Precisely, the concepts in axioms will be encoded into two semantic spaces defined in CosE. One is called angle-based semantic space, which is employed to preserve the transitivity or symmetry of relations in axioms. The other one is defined as translation-based semantic space that is used to measure the confidence of each axiom. We design two types of score functions for these two semantic spaces, so as to sufficiently learn the vector representations of concepts. Moreover, we propose a novel negative sampling strategy based on the mutual exclusion between $subclassOf$ and $disjointWith$. In this way, concepts can obtain better vector representations for schematic knowledge completion. We implement our method and verify it on four standard datasets generated by real ontologies. Experiments show that CosE can obtain better results than existing models and keep the logical properties of relations for transitivity and symmetry simultaneously. Full article

One of the essential points of food manufacturing in the industry and shelf life of the products is to improve the food traceability system. In recent years, the food traceability mechanism has become one of the emerging blockchain applications in order to improve the anti-counterfeiting area’s quality. Many food manufacturing systems have a low level of readability, scalability, and data accuracy. Similarly, this process is complicated in the supply chain and needs a lot of time for processing. The blockchain system creates a new ontology in the traceability system supply chain to deal with these issues. In this paper, a blockchain machine learning-based food traceability system (BMLFTS) is proposed in order to combine the new extension in blockchain, Machine Learning technology (ML), and fuzzy logic traceability system that is based on the shelf life management system for manipulating perishable food. The blockchain technology in the proposed system has been developed in order to address light-weight, evaporation, warehouse transactions, or shipping time. The blockchain data flow is designed to show the extension of ML at the level of food traceability. Finally, reliable and accurate data are used in a supply chain to improve shelf life. Full article

With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT. Full article

Although current estimates depict steady growth in Internet of Things (IoT), many works portray an as yet immature technology in terms of security. Attacks using low performance devices, the application of new technologies and data analysis to infer private data, lack of development in some aspects of security offer a wide field for improvement. The advent of Semantic Technologies for IoT offers a new set of possibilities and challenges, like data markets, aggregators, processors and search engines, which rise the need for security. New regulations, such as GDPR, also call for novel approaches on data-security, covering personal data. In this work, we present DS4IoT, a data-security ontology for IoT, which covers the representation of data-security concepts with the novel approach of doing so from the perspective of data and introducing some new concepts such as regulations, certifications and provenance, to classical concepts such as access control methods and authentication mechanisms. In the process we followed ontological methodologies, as well as semantic web best practices, resulting in an ontology to serve as a common vocabulary for data annotation that not only distinguishes itself from previous works by its bottom-up approach, but covers new, current and interesting concepts of data-security, favouring implicit over explicit knowledge representation. Finally, this work is validated by proof of concept, by mapping the DS4IoT ontology to the NGSI-LD data model, in the frame of the IoTCrawler EU project. Full article