Knowledge Representation Research Articles

Residency training programs to support residents working in First Nations, Inuit, and Métis communities

BackgroundTo gain culturally appropriate awareness of First Nations, Inuit and/or Métis Health, research suggests that programs focus on sending more trainees to First Nations, Inuit and/or Métis communities Working within this context provides experiences and knowledge that build upon classroom education and support trainees’ acquisition of skills to engage in culturally safe healthcare provision. This study examines residents' and faculty members' perceptions of how residency training programs can optimize First Nations, Inuit and/or Métis health training and support residents in gaining the knowledge, skills, and experiences for working in and with First Nations, Inuit and/or Métis communities.MethodsA qualitative approach was used, guided by a relational lens for collecting data and a constructivist grounded theory for data interpretation. Theoretical sampling was used to recruit 35 participants from three main study sites across two western Canadian provinces. Recruitment, data collection, and analysis using constructivist grounded theory occurred concurrently to ensure appropriate depth of exploration.ResultsOur data analysis revealed five themes: Five themes were generated: Complexity of voluntourism as a concept; Diversity of knowledge representation required for developing curriculum; Effective models of care for First Nations, Inuit and/or Métis health; Essential traits that residents should have for working in First Nations, Inuit and/or Métis communities; and Building relationships and trust by engaging the community.ConclusionsFirst Nations, Inuit and/or Métis Health should be prioritized within Canadian postgraduate medical education. Equipping trainees to provide holistic care, immersing in and learning from First Nations, Inuit and/or Métis communities is essential for developing the next generation of clinicians and preceptors. We present educational recommendations for residency programs to optimize First Nations, Inuit and/or Métis health educational experiences and provide residents with skills to provide effective and culturally safe care.

ManiNeg: Manifestation-guided multimodal pretraining for mammography screening.

Breast cancer poses a significant health threat worldwide. Contrastive learning has emerged as an effective method to extract critical lesion features from mammograms, thereby offering a potent tool for breast cancer screening and analysis. A crucial aspect of contrastive learning is negative sampling, where the selection of hard negative samples is essential for driving representations to retain detailed lesion information. In large-scale contrastive learning applied to natural images, it is often assumed that extracted features can sufficiently capture semantic content, and that each mini-batch inherently includes ideal hard negative samples. However, the unique characteristics of breast lumps challenge these assumptions when dealing with mammographic data. In response, we introduce ManiNeg, a novel approach that leverages manifestations as proxies to select hard negative samples. As a condensed representation of a physician's domain knowledge, manifestations represent observable symptoms or signs of a disease and can provide a robust basis for choosing hard negative samples. This approach benefits from its invariance to model optimization, facilitating efficient sampling. We tested ManiNeg on the task of distinguishing between benign and malignant breast lumps. Our results demonstrate that ManiNeg not only improves representation in both unimodal and multimodal contexts but also offers benefits that extend to datasets beyond the initial pretraining phase. To support ManiNeg and future research endeavors, we have developed the MVKL mammographic dataset. This dataset includes multi-view mammograms, corresponding reports, meticulously annotated manifestations, and pathologically confirmed benign-malignant outcomes for each case. The MVKL dataset and our codes are publicly available at https://github.com/wxwxwwxxx/ManiNeg to foster further research within the community.

Conceptual design of smart product-service system based on function-oriented search

Unlike traditional products, Smart Product-Service System design requires the broad design knowledge support. However, traditional knowledge representation does not consider the characteristic elements of Smart Product-Service System, which can lead to the loss of essential information when organising and reusing design knowledge. This limitation affects the application of cross-domain functional knowledge in the conceptual design of Smart Product-Service System. To address this issue, a process model for the conceptual design of Smart Product-Service System based on function-oriented search is proposed. An initial functional modelling is first constructed based on the characteristics of the Smart Product-Service System. The extended Function Behaviour Structure model is then used to construct a design knowledge model of the system. Based on Function-Oriented Search to search for patent knowledge with highly similar functions in the patent database, the knowledge fusion is used to solve the derived problems after pruning, so as to obtain high-quality system conceptual solutions. Finally, the effectiveness of the method is verified by taking the scheme construction of intelligent forklift product service system as an example.

Hierarchical Planning Applied to the Preliminary Design of CubeSats: Nanospace Study

ABSTRACTCubeSat design has been already studied and formalized but knowledge representation remains a challenge. The management of human‐learnt knowledge during the process is not an aspect that is often spoken about. This paper discusses the proposal of integrating a hierarchical planning approach with a model‐based one to the open‐source Nanospace framework, a web‐based application for concurrent engineering during the preliminary design phase of CubeSats. Hierarchical planning aids to introduce commonly tacit human expertise, an aspect that the preliminary design of CubeSats can benefit from. The proposed integration could allow a faster design convergence and faster inspection of candidate architectures The Nanospace framework itself may benefit from an approach bringing in model‐based efforts and hierarchical planning facilitate knowledge representation and reuse. A use case on the CREME CubeSat project is detailed, emphasizing how to impregnate the design iterations with experts' knowledge.

The cycle of underrepresentation: structural and institutional factors limiting the representation of Global South authors and knowledge in the IPCC

The cycle of underrepresentation: structural and institutional factors limiting the representation of Global South authors and knowledge in the IPCC

International Expert-Based Consensus Definition, Classification Criteria, and Minimum Data Elements for Osteoradionecrosis of the Jaw: An Inter-Disciplinary Modified Delphi Study.

Osteoradionecrosis of the jaw (ORNJ) is a severe iatrogenic disease characterized by bone death after radiation therapy (RT) to the head and neck. With over 9 published definitions and at least 16 classification systems, the true incidence and severity of ORNJ are obscured by lack of a standard for disease definition and severity assessment, leading to inaccurate estimation of incidence, reporting ambiguity, and likely under-diagnosis worldwide. This study aimed to achieve consensus on an explicit definition and phenotype of ORNJ and related precursor states through data standardization to facilitate effective diagnosis, monitoring, and multidisciplinary management of ORNJ. The ORAL Consortium comprised 69 international experts, including representatives from medical, surgical, radiation oncology, and oral/dental disciplines. Using a web-based modified Delphi technique, panelists classified descriptive cases using existing classification systems, reviewed systems for feature extraction and specification, and iteratively classified cases based on clinical/imaging feature combinations. The Consortium ORNJ definition was developed in alignment with SNOMED-CT terminology and recent ISOO-MASCC-ASCO guideline recommendations. Case review using existing ORNJ classification systems showed high rates of inability to classify (up to 76%). Ten consensus statements and nine minimum data elements (MDEs) were outlined for prospective collection and classification of precursor/ORNJ disease states. This study provides an international, consensus-based definition and MDE foundation for standardized ORNJ reporting in cancer survivors treated with RT. Head and neck surgeons, radiation, surgical, medical oncologists, and dental specialists should adopt MDEs to enable scalable health information exchange and analytics. Work is underway to develop both a human- and machine-readable knowledge representation for ORNJ (i.e., ontology) and multidisciplinary resources for dissemination to improve ORNJ reporting in academic and community practice settings.

Interweaving academic insights: advancing university knowledge management through a strategic data fabric framework

PurposeEffective knowledge management in large academic institutions is crucial for fostering innovation and improving educational practices. However, these institutions often face challenges, such as data fragmentation, siloed information systems and the complexity of integrating different data sources from various departments with complex hierarchical structures. To address these problems, the authors proposed a data fabric strategic framework that improves and enhances knowledge management by leveraging ontologies and knowledge graphs. This study aims to investigate the potential of knowledge graphs, ontological knowledge modelling and knowledge representation to improve knowledge management in large academic institutions. It also describes how technology can enhance knowledge accessibility and exchanges and improve decision-making processes based on insights from complex educational systems.Design/methodology/approachThis study uses coordination theory as a foundational framework to analyse intricate data systems in preparation for constructing, the Wizard of Oz method to facilitate the systematic organisation and management of information and the execution of an ontology-based data fabric framework and knowledge graphs. The authors propose a data fabric strategic framework aimed at improving knowledge management by leveraging ontologies and knowledge graphs.FindingsThe final evaluation demonstrates that this approach effectively breaks down data silos, promotes research collaboration and improves decision-making processes in large academic settings, offering solution-oriented data fabric technologies applicable to universities and university federations globally.Practical implicationsThe proposed system provides a more efficient way of managing and connecting fragmented academic resources, improving accessibility for both learners and educators. By interconnecting and streaming knowledge management process, the system can reduce not only operational costs but also expenses on doing scientific research.Originality/valueAcademic institutions prioritise time efficiency when acquiring vital data for improved scientific results. This emphasis extends beyond data governance to focus on how collective intelligence might improve organisational performance. The academic community has enhanced data utilisation through the implementation of data fabric technologies to improve data accessibility and data line tracking.

Systematic Review and Framework for AI-Driven Tacit Knowledge Conversion Methods and Machine Learning Algorithms for Ontology-Based Chatbots in E-Learning Platforms

The conversion of tacit knowledge, which is deeply rooted in personal experience and often difficult to articulate, presents a significant challenge within knowledge management systems. Ontology-based chatbots offer a promising solution by leveraging structured knowledge representations and advanced natural language processing (NLP) techniques to facilitate this transformation. This paper explores the various methods and algorithms used in developing ontology-based chatbots, with a particular focus on their role in converting tacit knowledge into more accessible forms. Additionally, it provides a comparative analysis of the algorithms employed, highlighting their respective strengths and weaknesses. Ultimately, this study addresses the critical challenge of managing and converting tacit knowledge, with the aim of enhancing the overall effectiveness of knowledge management systems.

A Comparative Analysis of European Media Coverage of the Israel–Gaza War Using Hesitant Fuzzy Linguistic Term Sets

Representing and interpreting human opinions within an unstructured framework is inherently complex. Hesitant fuzzy linguistic term sets offer a comprehensive context that facilitates a nuanced understanding of diverse perspectives. This study introduces a methodology that integrates sentiment analysis with hesitant fuzzy linguistic term sets to effectively aggregate and compare news from diverse sources. By employing linguistic scales, our approach enhances the interpretation of various perceptions and attitudes, facilitating comprehensive knowledge extraction and representation. The main objective of this research is to conduct a comparative analysis of news coverage across European countries in relation to the Israel–Gaza war. This analysis aims to capture the multifaceted sensitivities surrounding the ongoing situation, highlighting how different nations perceive the conflict.

Multiset Grammars as a Basic Knowledge Representation Model for Intelligent Systems Enabling Chemical Reactions Engineering

Application of multiset grammars as a knowledge representation model for intelligent systems, enabling analysis, optimization and design of complex chemical reactions, is proposed. This approach makes it possible to implant chemical knowledge bases into knowledge bases of intelligent systems, implementing smart planning and scheduling in cyberphysical industry, and thus to move to integrated end-to-end smart control of vast heterogeneous industrial clusters.

Research on the Sememe-Based Knowledge Representation Integrating Tibetan Features

The sememe, as the smallest semantic unit, is widely used in knowledge bases to represent world knowledge. HowNet is one of the most well-known sememe-based knowledge bases. In theory, the HowNet sememe system is applicable to all human languages and capable of expressing all world knowledge. However, this claim has yet to be verified. This paper uses specific instances to highlight issues with the HowNet sememe system when applied to Tibetan, such as vague definitions, unclear features, and the lack of sufficient sememes to describe various linguistic phenomena. The paper integrates the Tibetan controllable–uncontrollable relationship into the HowNet sememe system, refining its definitions and features, and subsequently modifies the HowNet knowledge dictionary based on this integration.

Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles.

This study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments. Decision rules, known for their transparency, provide an accessible method for knowledge extraction from data, facilitating decision-making processes across diverse fields. Traditional decision tree algorithms, such as CART and ID3, are employed to induce decision trees from bootstrapped datasets, which represent distributed data sources. Subsequently, a greedy algorithm is applied to derive decision rules that are true across multiple decision trees. Experiments are performed, taking into account knowledge representation and discovery perspectives. They show that, as the value of α, 0≤α<1, increases, shorter rules are obtained, and also it is possible to improve the classification accuracy of rule-based models.

Interval Methods in Knowledge Representation

Interval Methods in Knowledge Representation

A comprehensive review of digital twin in healthcare in the scope of simulative health-monitoring.

Digital twins (DTs) emerged in the wake of Industry 4.0 and the creation of cyber-physical systems, motivated by the increased availability and variability of machine and sensor data. DTs are a concept to create a digital representation of a physical entity and imitate its behavior, while feeding real-world data to the digital counterpart, thus allowing enabling digital simulations related to the real-world entity. The availability of new data sources raises the potential for developing structured approaches for prediction and analysis. Similarly, in the field of medicine and digital healthcare, the collection of patient-focused data is rising. Medical DTs, a new concept of structured, exchangeable representations of knowledge, are increasingly used for capturing personal health, targeting specific illnesses, or addressing complex healthcare scenarios in hospitals. This article surveys the current state-of-the-art in applying DTs in healthcare, and how these twins are generated to support smart, personalized medicine. These concepts are applied to a DT for a simulated health-monitoring scenario. The DT use case is implemented using AnyLogic multi-agent simulation, monitoring the patient's personal health indicators and their development. The results indicate both possibilities and challenges and provide important insights for future DT implementations in healthcare. They have the potential to optimize healthcare in various ways, such as providing patient-centered health-monitoring.

Emotional distress and affective knowledge representation one year after the COVID-19 outbreak.

This study examines whether the detrimental effects of the COVID-19 pandemic on the affectivity of the population extend one year after the outbreak. In an online-mobile session, participants completed surveys (i.e., demographic characteristics, positive-negative affectivity, interoceptive awareness) and a similarity judgment task of triplets of emotional concepts, from which we derived 2D maps of their affective knowledge representation. Compared with pre-pandemic data derived from a comparable population, we report three main findings. First, we observed enhanced negative affectivity during the pandemic, but no changes in positive affectivity levels. Second, increased self-reported interoceptive awareness compared to pre-pandemic data, with greater attention to bodily sensations and adaptive aspects of interoceptive sensitivity. Furthermore, female participants reported higher scores than males on the questionnaire subscales of Emotional Awareness and Attention Regulation. Third, the effect of pandemic-related conditions is also apparent in the mental organization of emotional concepts, especially for female participants (i.e., reduced coherence in the organization of the concepts along the arousal dimension and more misclassification of concepts based on arousal) and participants who did not perform physical activity (a collapse of the arousal dimension). Some of the effects of the pandemic, thus, persist about a year after the outbreak. These results advise providing programs of psychological and emotional assistance throughout the pandemic beyond the outbreak, and that age-dependent gender differences should be accounted for to define tailored interventions. Physical activity might relieve pandemic-related stressors, so it should be promoted during particularly stressful periods for the population.

Understanding Knowledge Representation Concepts in Machine Translation: A Review

Understanding Knowledge Representation Concepts in Machine Translation: A Review

Операторы библиотеки и эпистемология искусственного интеллекта

The article dwells on the origins of the concept of artificial intelligence as applied to the library with its principles of fast search. The integrity of such knowledge, corresponding to the integrity of the products of neural networks, is characterized. The history of science has seen not only the management of discoveries, but also the operation of such specific production, manifested vividly in the activities of women mathematicians and women philosophers. They are operators rather than inventors of networks, who integrate networks and ensure that different epistemic regimes can be integrated. In this paper, the historical outline of the emergence of the library as a model of artificial intelligence is discussed. The ancient regulations of Aristotle and the Library of Alexandria stipulated a purely alphabetical rather than thematic ordering principle; at that time, the production of knowledge required visual aids, integral formulae, and the decomposition of knowledge into indications of sources without remainder. An ideal operator of such a library was Hypatia of Alexandria. Further development of libraries involved the production of corporate, religious, and national knowledge, unlocked by new operators through returning universal meaning to the visual representation of knowledge. Currently, such universal visual representation is characteristic of images generated by artificial intelligence. Knowledge production modes require functions of both direct producers and recipients of knowledge. The development of sciences during different periods has made it necessary to encompass the knowable as well as to recognize its agency and ability to become ready for use. We see the origins of such agency in primitive initiation and view the library as a mechanism of potential initiation. In that case, we need not only knowledge distributors that make potential initiation real, but also knowledge operators that make it possible to move from harsher to softer modes of initiation. The article points out the traits that an individual acting as a reliable operator of such knowledge can have. Recognizing the agency of libraries allows us to plan the use of neural networks, including for countering fake news.

Knowledge Reasoning- and Progressive Distillation-Integrated Detection of Electrical Construction Violations.

To address the difficulty in detecting workers' violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. First, standards, norms, and guidelines in the field of electric power construction are collected to build a comprehensive knowledge graph, aiming to provide accurate knowledge representation and normative analysis. Then, the knowledge graph is combined with the object-detection model in the form of triplets, where detected objects and their interactions are represented as subject-predicate-object relationship. These triplets are embedded into the model using an adaptive connection network, which dynamically weights the relevance of external knowledge to enhance detection accuracy. Furthermore, to enhance the model's performance, the paper designs a progressive multi-level distillation strategy. On one hand, knowledge transfer is conducted at the object level, region level, and global level, significantly reducing the loss of contextual information during distillation. On the other hand, two teacher models of different scales are introduced, employing a two-stage distillation strategy where the advanced teacher guides the primary teacher in the first stage, and the primary teacher subsequently distills this knowledge to the student model in the second stage, effectively bridging the scale differences between the teacher and student models. Experimental results demonstrate that under the proposed method, the model size is reduced from 14.5 MB to 3.8 MB, and the floating-point operations (FLOPs) are reduced from 15.8 GFLOPs to 5.9 GFLOPs. Despite these optimizations, the AP50 reaches 92.4%, showing a 1.8% improvement compared to the original model. These results highlight the method's effectiveness in accurately detecting workers' violation behaviors, providing a quantitative basis for its superiority and offering a novel approach for safety management and monitoring at construction sites.

Deep Learning Algorithm Design for Discovery and Dysfunction of Landmines

Deep Learning is a cutting-edge technology which has a noteworthy impact in the real-world applications. The multi-layer neural nets involved in the blueprint of deep learning enables it to deliver a comprehensive decision-making system with quality of “think alike human cerebrum”. Deep Learning assumes an essential part in various fields like horticulture, medication, substantial business and so forth. Deep Learning can be well prompted in the remote sensing applications especially in perilous military applications. The location of land mines can be detected using a deep learning algorithm design technique aided with distinctive machine learning tools and techniques. The intelligent system designed by the deep learning process involves a massive dataset including the assorted features of the landmines like size, sort, dampness, ground profundity and so on. Incorporation of Geographical Information System can give a prevalent statistical analysis of the varied landmines. The multiple layers present in the deep learning neural schema may increase the feature extraction and the knowledge representation through increase in the complexities of landmines’ input sets. The likelihood of brokenness of landmines can be increased by the utilization of deep learning prediction model which enormously helps the survival of militaries, creating a social effect.

Artificial Intelligence as a Pedagogical Tool for Architectural Design Education

Artificial intelligence (AI) is increasingly influencing art and design, with tools like ChatGPT, DALL.E, and Midjourney transforming the way designers create visual content from written descriptions. Since its launch, AI-powered systems have sparked widespread interest and expanded production practices in design and artistic creativity. This transition has ushered in a new era of co-authorship, in which human designers and AI systems collaborate to reshape the boundaries of design. The introduction of language-based diffusion models has enabled a harmonic integration of language and visual elements, resulting in creative design paradigms. However, this incorporation of AI raises serious concerns about the cultural and social relevance of AI-generated designs, which may become estranged from human contexts if heavily dependent on algorithms. The representation of architectural knowledge is also evolving, as AI allows for a return to textual and mathematical tools, rather than traditional visual representation methods. This transition creates substantial challenges and possibilities for architectural education, particularly in design studios. The integration of AI into design curricula has the potential to reshape architectural education, necessitating that educators adapt to technology advances. This study investigates these developments, providing insights into the evolving landscape of design pedagogy in the age of AI and contributing to the continuing debate about the role of AI in design and architecture.