Big Data and Cognitive Computing最新文献

{"title":"SemNet Explorer: An Evidence-Grounded Knowledge Graph-LLM Framework for Multi-Scale Mechanistic Reporting Across Biomedical Domains.","authors":"Xin He, David Camacho, Lama Moukheiber, Meghna Iyer, Benjamin Zhao, Christophe Ye, Batuhan Nursal, Xinyu Guo, Albert J B Lee, Cassie S Mitchell","doi":"10.3390/bdcc10060171","DOIUrl":"10.3390/bdcc10060171","url":null,"abstract":"<p><strong>Background: </strong>Mechanistic reporting from large-scale biomedical knowledge graphs remains challenging, particularly when integrating structured graph evidence with large language model (LLM)-based explanation in a reproducible and auditable manner. Existing approaches either rely on manual synthesis of graph-derived results or generate unconstrained narratives that lack traceability to underlying evidence.</p><p><strong>Methods: </strong>We present <b>SemNet Explorer</b>, an evidence-grounded knowledge graph-LLM unified framework for automated mechanistic reporting across biomedical domains using SemNet 2.0, a PubMed-scale heterogeneous knowledge graph. Given a set of target concepts and a selected semantic layer, the framework organizes graph-derived evidence into structured regions and generates two complementary report types: global reports for process-level mechanisms and anchor-centric reports for localized mediator-based explanations. A central methodological contribution is an ablation-derived adaptive grounding policy: we systematically compare alternative evidence-integration strategies across report types, semantic layers, and region structures, and use the resulting preferences to guide prompt selection in the deployed system.</p><p><strong>Results: </strong>SemNet Explorer produces stable region decompositions and interpretable report scaffolds across molecular (AAPP), disease-level (DSYN), and pharmacologic (PHSU) representations. For global reports, explicit evidence grounding improves expression quality more consistently than content accuracy, with benefits dependent on evidence density and semantic abstraction. In contrast, anchor-centric reports show consistent improvements in both content and expression under stronger, mediator-constrained prompting. These findings are supported by both pairwise ablation comparisons and absolute score analyses.</p><p><strong>Conclusions: </strong>SemNet Explorer establishes a generalizable unified framework and interactive platform for transforming knowledge graph evidence into reproducible mechanistic narratives across biomedical domains, including multimorbidity analysis, comparative pathophysiology, drug repurposing, and adverse event discovery. The results demonstrate that effective knowledge graph-LLM integration requires adaptive, context-dependent evidence grounding rather than fixed prompting strategies.</p>","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"10 6","pages":"171"},"PeriodicalIF":5.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13596072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Source Robust Non-Parametric Reconstruction of Epidemic-like Event-Based Network Diffusion Processes Under Online Data.","authors":"Jiajia Xie, Chen Lin, Xinyu Guo, Cassie S Mitchell","doi":"10.3390/bdcc9100262","DOIUrl":"10.3390/bdcc9100262","url":null,"abstract":"<p><p>Temporal network diffusion models play a crucial role in healthcare, information technology, and machine learning, enabling the analysis of dynamic event-based processes such as disease spread, information propagation, and behavioral diffusion. This study addresses the challenge of reconstructing temporal network diffusion events in real time under conditions of missing and evolving data. A novel non-parametric reconstruction method by simple weights differentiationis proposed to enhance source detection robustness with provable improved error bounds. The approach introduces adaptive cost adjustments, dynamically reducing high-risk source penalties and enabling bounded detours to mitigate errors introduced by missing edges. Theoretical analysis establishes enhanced upper bounds on false positives caused by detouring, while a stepwise evaluation of dynamic costs minimizes redundant solutions, resulting in robust Steiner tree reconstructions. Empirical validation on three real-world datasets demonstrates a 5% improvement in Matthews correlation coefficient (MCC), a twofold reduction in redundant sources, and a 50% decrease in source variance. These results confirm the effectiveness of the proposed method in accurately reconstructing temporal network diffusion while improving stability and reliability in both offline and online settings.</p>","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"9 10","pages":"262"},"PeriodicalIF":5.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13596075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Performance of Topic Modeling Techniques with Human Validation to Support Qualitative Analysis","authors":"J. Romero, Miguel A. Feijoo‐Garcia, Gaurav Nanda, Brittany Newell, Alejandra J. Magana","doi":"10.3390/bdcc8100132","DOIUrl":"https://doi.org/10.3390/bdcc8100132","url":null,"abstract":"Examining the effectiveness of machine learning techniques in analyzing engineering students’ decision-making processes through topic modeling during simulation-based design tasks is crucial for advancing educational methods and tools. Thus, this study presents a comparative analysis of different supervised and unsupervised machine learning techniques for topic modeling, along with human validation. Hence, this manuscript contributes by evaluating the effectiveness of these techniques in identifying nuanced topics within the argumentation framework and improving computational methods for assessing students’ abilities and performance levels based on their informed decisions. This study examined the decision-making processes of engineering students as they participated in a simulation-based design challenge. During this task, students were prompted to use an argumentation framework to articulate their claims, evidence, and reasoning, by recording their informed design decisions in a design journal. This study combined qualitative and computational methods to analyze the students’ design journals and ensured the accuracy of the findings through the researchers’ review and interpretations of the results. Different machine learning models, including random forest, SVM, and K-nearest neighbors (KNNs), were tested for multilabel regression, using preprocessing techniques such as TF-IDF, GloVe, and BERT embeddings. Additionally, hyperparameter optimization and model interpretability were explored, along with models like RNNs with LSTM, XGBoost, and LightGBM. The results demonstrate that both supervised and unsupervised machine learning models effectively identified nuanced topics within the argumentation framework used during the design challenge of designing a zero-energy home for a Midwestern city using a CAD/CAE simulation platform. Notably, XGBoost exhibited superior predictive accuracy in estimating topic proportions, highlighting its potential for broader application in engineering education.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"8 10","pages":"132-132"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2504-2289/8/10/132/pdf?version=1728454486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147895741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Almost Nobody Is Using ChatGPT to Write Academic Science Papers (Yet).","authors":"Heather Desaire, Madeline Isom, David Hua","doi":"10.3390/bdcc8100133","DOIUrl":"https://doi.org/10.3390/bdcc8100133","url":null,"abstract":"<p><p>We assessed 19,000 scientific introductions to measure the level of undisclosed use of ChatGPT in scientific papers published in 2023 and early 2024. We applied a \"stylistics\" approach that has previously been shown to be effective at differentiating AI-generated text from human-written text in a variety of venues. Ten different MDPI journals were selected for this study, and the rate of use of undisclosed AI writing in these journals was fairly consistent across the journals. We estimate that ChatGPT was used for writing or significant editing in about 1 to 3% of the introductions tested. This analysis is the first systematic study of detecting undisclosed ChatGPT in published manuscripts in cases where obvious indicators, such as phrases like \"regenerate response\", are not present. The work demonstrates that generative AI is not polluting mainstream journals to any appreciable extent and that the overwhelming majority of scientists remain hesitant to embrace this tool for late-stage writing and editing.</p>","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"8 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12382442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey of Incremental Deep Learning for Defect Detection in Manufacturing","authors":"R. Mohandas, Mark Southern, Eoin O’Connell, Martin J Hayes","doi":"10.3390/bdcc8010007","DOIUrl":"https://doi.org/10.3390/bdcc8010007","url":null,"abstract":"Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedures to dynamically update model-based detection methods that use sequential streaming during the training phase. This paper reviews how new process, training or validation information is rigorously incorporated in real time when detection exceptions arise during inspection. In particular, consideration is given to how new tasks, classes or decision pathways are added to existing models or datasets in a controlled fashion. An analysis of studies from the incremental learning literature is presented, where the emphasis is on the mitigation of process complexity challenges such as, catastrophic forgetting. Further, practical implementation issues that are known to affect the complexity of deep learning model architecture, including memory allocation for incoming sequential data or incremental learning accuracy, is considered. The paper highlights case study results and methods that have been used to successfully mitigate such real-time manufacturing challenges.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"78 22","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model","authors":"Zuchao Li, Min Peng, Marcos Orellana, Patricio Santiago García, Guillermo Daniel Ramon, Jorge Luis Zambrano-Martinez, Andrés Patiño-León, María Verónica Serrano, Priscila Cedillo","doi":"10.3390/bdcc8010003","DOIUrl":"https://doi.org/10.3390/bdcc8010003","url":null,"abstract":"Health problems in older adults lead to situations where communication with peers, family and caregivers becomes challenging for seniors; therefore, it is necessary to use alternative methods to facilitate communication. In this context, Augmentative and Alternative Communication (AAC) methods are widely used to support this population segment. Moreover, with Artificial Intelligence (AI), and specifically, machine learning algorithms, AAC can be improved. Although there have been several studies in this field, it is interesting to analyze common phrases used by seniors, depending on their context (i.e., slang and everyday expressions typical of their age). This paper proposes a semantic analysis of the common phrases of older adults and their corresponding meanings through Natural Language Processing (NLP) techniques and a pre-trained language model using semantic textual similarity to represent the older adults’ phrases with their corresponding graphic images (pictograms). The results show good scores achieved in the semantic similarity between the phrases of the older adults and the definitions, so the relationship between the phrase and the pictogram has a high degree of probability.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"47 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BNMI-DINA: A Bayesian Cognitive Diagnosis Model for Enhanced Personalized Learning","authors":"Yiming Chen, Shuang Liang","doi":"10.3390/bdcc8010004","DOIUrl":"https://doi.org/10.3390/bdcc8010004","url":null,"abstract":"In the field of education, cognitive diagnosis is crucial for achieving personalized learning. The widely adopted DINA (Deterministic Inputs, Noisy And gate) model uncovers students’ mastery of essential skills necessary to answer questions correctly. However, existing DINA-based approaches overlook the dependency between knowledge points, and their model training process is computationally inefficient for large datasets. In this paper, we propose a new cognitive diagnosis model called BNMI-DINA, which stands for Bayesian Network-based Multiprocess Incremental DINA. Our proposed model aims to enhance personalized learning by providing accurate and detailed assessments of students’ cognitive abilities. By incorporating a Bayesian network, BNMI-DINA establishes the dependency relationship between knowledge points, enabling more accurate evaluations of students’ mastery levels. To enhance model convergence speed, key steps of our proposed algorithm are parallelized. We also provide theoretical proof of the convergence of BNMI-DINA. Extensive experiments demonstrate that our approach effectively enhances model accuracy and reduces computational time compared to state-of-the-art cognitive diagnosis models.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" 3","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139142073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-Based and Generative-AI-Driven Pedagogical Conversational Agents: A Comparative Study of Grice’s Cooperative Principles and Trust","authors":"Matthias Wölfel, Mehrnoush Barani Shirzad, Andreas Reich, Katharina Anderer","doi":"10.3390/bdcc8010002","DOIUrl":"https://doi.org/10.3390/bdcc8010002","url":null,"abstract":"The emergence of generative language models (GLMs), such as OpenAI’s ChatGPT, is changing the way we communicate with computers and has a major impact on the educational landscape. While GLMs have great potential to support education, their use is not unproblematic, as they suffer from hallucinations and misinformation. In this paper, we investigate how a very limited amount of domain-specific data, from lecture slides and transcripts, can be used to build knowledge-based and generative educational chatbots. We found that knowledge-based chatbots allow full control over the system’s response but lack the verbosity and flexibility of GLMs. The answers provided by GLMs are more trustworthy and offer greater flexibility, but their correctness cannot be guaranteed. Adapting GLMs to domain-specific data trades flexibility for correctness.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"102 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139157165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Bayesian Inference for Large-Scale IoT Systems","authors":"Eleni Vlachou, Aristeidis Karras, Christos N. Karras, Leonidas Theodorakopoulos, C. Halkiopoulos, S. Sioutas","doi":"10.3390/bdcc8010001","DOIUrl":"https://doi.org/10.3390/bdcc8010001","url":null,"abstract":"In this work, we present a Distributed Bayesian Inference Classifier for Large-Scale Systems, where we assess its performance and scalability on distributed environments such as PySpark. The presented classifier consistently showcases efficient inference time, irrespective of the variations in the size of the test set, implying a robust ability to handle escalating data sizes without a proportional increase in computational demands. Notably, throughout the experiments, there is an observed increase in memory usage with growing test set sizes, this increment is sublinear, demonstrating the proficiency of the classifier in memory resource management. This behavior is consistent with the typical tendencies of PySpark tasks, which witness increasing memory consumption due to data partitioning and various data operations as datasets expand. CPU resource utilization, which is another crucial factor, also remains stable, emphasizing the capability of the classifier to manage larger computational workloads without significant resource strain. From a classification perspective, the Bayesian Logistic Regression Spark Classifier consistently achieves reliable performance metrics, with a particular focus on high specificity, indicating its aptness for applications where pinpointing true negatives is crucial. In summary, based on all experiments conducted under various data sizes, our classifier emerges as a top contender for scalability-driven applications in IoT systems, highlighting its dependable performance, adept resource management, and consistent prediction accuracy.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" 7","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138962058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extraction of Significant Features by Fixed-Weight Layer of Processing Elements for the Development of an Efficient Spiking Neural Network Classifier","authors":"A. Sboev, R. Rybka, Dmitry Kunitsyn, A. Serenko, Vyacheslav Ilyin, Vadim V Putrolaynen","doi":"10.3390/bdcc7040184","DOIUrl":"https://doi.org/10.3390/bdcc7040184","url":null,"abstract":"In this paper, we demonstrate that fixed-weight layers generated from random distribution or logistic functions can effectively extract significant features from input data, resulting in high accuracy on a variety of tasks, including Fisher’s Iris, Wisconsin Breast Cancer, and MNIST datasets. We have observed that logistic functions yield high accuracy with less dispersion in results. We have also assessed the precision of our approach under conditions of minimizing the number of spikes generated in the network. It is practically useful for reducing energy consumption in spiking neural networks. Our findings reveal that the proposed method demonstrates the highest accuracy on Fisher’s iris and MNIST datasets with decoding using logistic regression. Furthermore, they surpass the accuracy of the conventional (non-spiking) approach using only logistic regression in the case of Wisconsin Breast Cancer. We have also investigated the impact of non-stochastic spike generation on accuracy.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"93 3","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138995075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}