Federated learning is a knowledge transmission and training process that occurs in turn between user models on edge devices and the training model in the central server. Due to privacy policies and concerns and heterogeneous data, this is a widesprea...
The judicious configuration of predicates is a crucial but often overlooked aspect in the field of knowledge graphs. While previous research has primarily focused on the precision of triples in assessing knowledge graph quality, the rationality of pr...
Today, the automotive and transportation sector is undergoing a transformation process to meet the requirements of sustainable and efficient operations. This transformation mainly reveals itself by electric vehicles, hybrid electric vehicles, and ele...
This paper proposes a framework to extract knowledge-management elements from business systems in healthcare organizations. According to results of in-depth interviews with experts in the field, a framework is defined, and software was developed to g...
In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal d...
Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor k...
13 February 2018
The smart and sustainable cities concept has become a globally hot topic during the last few decades, particularly following the catastrophic impacts of global climate change. The assessment of smart and sustainable cities, however, is a major challe...
Suppressing knowledge hiding is a prerequisite for achieving positive knowledge interactions among people. Most previous studies concentrate on knowledge hiding in organizations, but the quantitative examination of knowledge hiding antecedents in the...
The constantly updating big data in the ocean engineering domain has challenged the traditional manner of manually extracting knowledge, thereby underscoring the current absence of a knowledge graph framework in such a special field. This paper propo...
This paper reveals the research hotspots and development directions of case-based reasoning in the field of health care, and proposes the framework and key technologies of medical knowledge service systems based on case-based reasoning (CBR) in the b...
This study proposes the integration of change management with a knowledge management framework to address knowledge retention and successful change management in the context of Industry 5.0. Using the ADKAR model, it is suggested to implement strateg...
Construction safety requirements (SRs), which serve as critical information encapsulating a wide range of safety-related issues, constitute a fundamental basis for effective construction safety management. The constraints of the complex information c...
Sustainable knowledge sharing among mathematics and science teachers is imperative to improve the ability of such teachers to enhance the way information is transferred to learners. South Africa ranked 37th out of 42 countries in an assessment to det...
Market convergence challenges socially sustainable supply chain management (SSSCM) due to the increasing competition. Identifying market convergence trends allows companies to respond quickly to market changes and improve supply chain resilience (SCR...
Knowledge management (KM) plays a key role in the success of several enterprise systems, such as supply chain management (SCM). This paper discusses knowledge-enabled supply chain management (K-SCM) as it enhances the organizations’ expertise in SCM....
Vernacular architecture represents the traditional architecture that developed over time within a particular culture or region that embodied indigenous knowledge. These buildings provide an invaluable cultural heritage, and learning from them is an i...
This article presents a state-of-the-art system to extract and synthesize causal statements from company reports into a directed causal graph. The extracted information is organized by its relevance to different stakeholder group benefits (customers,...
Text-based visual question answering (TextVQA) requires reasoning over images containing rich textual content, often involving knowledge beyond what is directly observable. Existing methods fuse visual objects and OCR tokens but struggle when questio...
Rural territories continue to face persistent structural challenges related to depopulation, limited economic diversification, and unequal access to specialized knowledge. Although scientific research and applied expertise are widely recognized as cr...
In recent years, the rapid advancement of large language models (LLMs) has driven significant breakthroughs in artificial intelligence. Leveraging LLMs in conjunction with domain-specific knowledge to develop intelligent assistants can reduce operati...
Over the last decades, to better proceed towards global and local policy goals, there was an increasing demand for the scientific community to support decision-makers with the best available knowledge. Scientific modeling is key to enable the transit...
Geographical knowledge graphs (GeoKGs) have long experienced several fundamental challenges in representing complex spatial relationships, such as limited dimensionality, insufficient quantification of relationship strength, and weak reasoning capabi...
Automatic identification of worker safety violations can substantially strengthen construction-site safety management by enabling continuous, real-time monitoring. Although recent advances have made automated detection feasible, many existing systems...
In capital-intensive industries, operational knowledge is often trapped in unstructured technical manuals, creating a barrier to efficient and reliable maintenance planning. This work addresses the need for an integrated system that can automate know...
To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph co...
The importance of highly monitored and analyzed processes, linked by information systems such as knowledge graphs, is growing. In addition, the integration of operators has become urgent due to their high costs and from a social point of view. An app...
This paper investigates how participatory knowledge production may contribute to more democratic sustainability governance. It develops an analytical framework in order to perform a systematic analysis of the GammaSense project in the Netherlands, on...
4 April 2023
In 2015, the Strategic Alliance adopted the International Classification of Functioning, Disability, and Health (ICF) as the disablement model framework for delivery of and communication about patient care in athletic training. The purpose of this st...
A novel framework called TravelRAG is introduced in this paper, which is built upon a large language model (LLM) and integrates Retrieval-Augmented Generation (RAG) with knowledge graphs to create a retrieval system framework designed for the tourism...
Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenario...
The Retrieval-Augmented Generation (RAG) framework enhances Large Language Models (LLMs) by retrieving relevant knowledge to broaden their knowledge boundaries and mitigate factual hallucinations stemming from knowledge gaps. However, the RAG Framewo...
This paper takes jacket as an example, constructs a clothing knowledge model based on domain ontology, and explores ways to represent and reuse clothing knowledge in the context of the Internet economy. In this paper, jacket knowledge is sorted out b...
Modern robotic systems are evolving toward conducting missions based on semantic knowledge. Such systems require environmental modeling as essential for successful mission execution. However, there is an inefficiency in that manual modeling is requir...
Blockchain technology offers promising solutions to some of the challenges that IKS face, such as secure long-term preservation. This study aims to develop a blockchain-based framework for sustainable management and preservation of Indigenous knowled...
Background: Steel box girders are widely employed in bridge engineering due to their excellent mechanical properties and construction convenience, yet their modular design still encounters bottlenecks such as knowledge reuse difficulties and informat...
A hybrid modelling framework is proposed to predict endpoint sulphur content in the ladle furnace (LF) refining process by embedding metallurgical expert knowledge into interpretable machine learning (ML). Industrial process data were extracted from...
In recent years, the Markov Logic Network (MLN) has emerged as a powerful tool for knowledge-based inference due to its ability to combine first-order logic inference and probabilistic reasoning. Unfortunately, current MLN solutions cannot efficientl...
Digital twins (DTs) have become a central paradigm for modeling cyber–physical systems and digital infrastructures, yet the term is applied to very different representations—from physical assets to operational processes and service enviro...
Professional knowledge is highlighted as an important prerequisite of both medical doctors and teachers. Based on recent conceptions of professional knowledge in these fields, knowledge can be differentiated within several aspects. However, these kno...
Narrative extraction builds coherent ordered sequences of documents that trace how concepts develop over time, and is a growing area of information retrieval. In this work we focus on scientific literature, using a corpus of 3549 IEEE visualization r...
Knowledge distillation serves as a prevalent model compression strategy within scene text detection, enabling the transfer of learned representations from a high-capacity teacher architecture to a streamlined student counterpart. Building upon this c...
Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is o...
The multi-modal knowledge graph completion (MMKGC) task aims to automatically mine the missing factual knowledge from the existing multi-modal knowledge graphs (MMKGs), which is crucial in advancing cross-modal learning and reasoning. However, few me...
The traditional model training approach based on negative sampling randomly samples a portion of negative samples for training, which can easily overlook important negative samples and adversely affect the training of knowledge graph embedding models...
Sleep disorders are prevalent across the population and, in older adults, are associated with cognitive decline, reduced functional capacity, and diminished quality of life. While polysomnography (PSG) provides detailed neurophysiological information...
Territorial ecological restoration involves a continuous chain of tasks, including degradation diagnosis, target identification, zoning-based governance, process monitoring, effectiveness assessment and adaptive management. The relevant knowledge is...
Accurate trajectory prediction of surrounding vehicles is a fundamental challenge in autonomous driving, requiring sophisticated modeling of complex vehicle interactions, traffic dynamics, and contextual dependencies. This paper introduces Adaptive S...
This study introduces a comprehensive knowledge graph (KG)-based framework designed to support sustainable decision-making by integrating, enriching, and analyzing heterogeneous data sources. The proposed methodology leverages domain expertise, real-...
This study conducts a systematic bibliometric review of artificial intelligence (AI)-based approaches to tacit knowledge extraction and management. Drawing on data retrieved from Scopus and Web of Science, this study analyzes 126 publications publish...
Against the backdrop of global biodiversity crises and climate change, integrated forest management (IFM) has emerged as a critical pathway for sustainable forest development. Grounded in the social–ecological system (SES) framework, we examine...
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