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Journal articles on the topic 'Knowledge graph refinement'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Zhang, Dehai, Menglong Cui, Yun Yang, et al. "Knowledge Graph-Based Image Classification Refinement." IEEE Access 7 (2019): 57678–90. http://dx.doi.org/10.1109/access.2019.2912627.

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Hsueh, Huei-Chia, Shuo-Chen Chien, Chih-Wei Huang, et al. "A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications." PLOS ONE 19, no. 1 (2024): e0296939. http://dx.doi.org/10.1371/journal.pone.0296939.

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Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validatio

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Kayali, Moe, and Dan Suciu. "Quasi-Stable Coloring for Graph Compression." Proceedings of the VLDB Endowment 16, no. 4 (2022): 803–15. http://dx.doi.org/10.14778/3574245.3574264.

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We propose quasi-stable coloring , an approximate version of stable coloring. Stable coloring, also called color refinement, is a well-studied technique in graph theory for classifying vertices, which can be used to build compact, lossless representations of graphs. However, its usefulness is limited due to its reliance on strict symmetries. Real data compresses very poorly using color refinement. We propose the first, to our knowledge, approximate color refinement scheme, which we call quasi-stable coloring. By using approximation, we alleviate the need for strict symmetry, and allow for a tr

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Paulheim, Heiko. "Knowledge graph refinement: A survey of approaches and evaluation methods." Semantic Web 8, no. 3 (2016): 489–508. http://dx.doi.org/10.3233/sw-160218.

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Zhang, Yichong, and Yongtao Hao. "Traditional Chinese Medicine Knowledge Graph Construction Based on Large Language Models." Electronics 13, no. 7 (2024): 1395. http://dx.doi.org/10.3390/electronics13071395.

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This study explores the use of large language models in constructing a knowledge graph for Traditional Chinese Medicine (TCM) to improve the representation, storage, and application of TCM knowledge. The knowledge graph, based on a graph structure, effectively organizes entities, attributes, and relationships within the TCM domain. By leveraging large language models, we collected and embedded substantial TCM–related data, generating precise representations transformed into a knowledge graph format. Experimental evaluations confirmed the accuracy and effectiveness of the constructed graph, ext

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Zeng, Xianlin, Yufeng Wang, Yuqi Sun, Guodong Guo, Wenrui Ding, and Baochang Zhang. "Graph Structure Refinement with Energy-based Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22326–35. https://doi.org/10.1609/aaai.v39i21.34388.

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Graph Neural Networks (GNNs) have recently gained widespread attention as a successful tool for analyzing graph-structured data. However, imperfect graph structure with noisy links lacks enough robustness and may damage graph representations, therefore limiting the GNNs' performance in practical tasks. Moreover, existing generative architectures fail to fit discriminative graph-related tasks. To tackle these issues, we introduce an unsupervised method based on a joint of generative training and discriminative training to learn graph structure and representation, aiming to improve the discrimin

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Denford, Mark, Andrew Solomon, John Leaney, and Tim Neill. "Architectural Abstraction as Transformation of Poset Labelled Graphs." JUCS - Journal of Universal Computer Science 10, no. (10) (2004): 1408–28. https://doi.org/10.3217/jucs-010-10-1408.

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The design of large, complex computer based systems, based on their architecture, will benefit from a formal system that is intuitive, scalable and accessible to practitioners. The work herein is based in graphs which are an efficient and intuitive way of encoding structure, the essence of architecture. A model of system architectures and architectural abstraction is proposed, using poset labelled graphs and their transformations. The poset labelled graph formalism closely models several important aspects of architectures, namely topology, type and levels of abstraction. The technical merits o

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Aldughayfiq, Bader, Farzeen Ashfaq, N. Z. Jhanjhi, and Mamoona Humayun. "Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB." Healthcare 11, no. 12 (2023): 1762. http://dx.doi.org/10.3390/healthcare11121762.

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Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge

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Choi, Seungmin, and Yuchul Jung. "Knowledge Graph Construction: Extraction, Learning, and Evaluation." Applied Sciences 15, no. 7 (2025): 3727. https://doi.org/10.3390/app15073727.

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A Knowledge Graph (KG), which structurally represents entities (nodes) and relationships (edges), offers a powerful and flexible approach to knowledge representation in the field of Artificial Intelligence (AI). KGs have been increasingly applied in various domains—such as natural language processing (NLP), recommendation systems, knowledge search, and medical diagnostics—spurring continuous research on effective methods for their construction and maintenance. Recently, efforts to combine large language models (LLMs), particularly those aimed at managing hallucination symptoms, with KGs have g

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Dong, Qian, Shuzi Niu, Tao Yuan, and Yucheng Li. "Disentangled Graph Recurrent Network for Document Ranking." Data Science and Engineering 7, no. 1 (2022): 30–43. http://dx.doi.org/10.1007/s41019-022-00179-3.

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AbstractBERT-based ranking models are emerging for its superior natural language understanding ability. All word relations and representations in the concatenation of query and document are modeled in the self-attention matrix as latent knowledge. However, some latent knowledge has none or negative effect on the relevance prediction between query and document. We model the observable and unobservable confounding factors in a causal graph and perform do-query to predict the relevance label given an intervention over this graph. For the observed factors, we block the back door path by an adaptiv

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Fauceglia, Nicolas, Mustafa Canim, Alfio Gliozzo, et al. "KAAPA: Knowledge Aware Answers from PDF Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 16029–31. http://dx.doi.org/10.1609/aaai.v35i18.18002.

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We present KaaPa (Knowledge Aware Answers from Pdf Analysis), an integrated solution for machine reading comprehension over both text and tables extracted from PDFs. KaaPa enables interactive question refinement using facets generated from an automatically induced Knowledge Graph. In addition it provides a concise summary of the supporting evidence for the provided answers by aggregating information across multiple sources. KaaPa can be applied consistently to any collection of documents in English with zero domain adaptation effort. We showcase the use of KaaPa for QA on scientific literature

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Koutra, Danai. "The power of summarization in graph mining and learning." Proceedings of the VLDB Endowment 14, no. 13 (2021): 3416. http://dx.doi.org/10.14778/3484224.3484238.

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Our ability to generate, collect, and archive data related to everyday activities, such as interacting on social media, browsing the web, and monitoring well-being, is rapidly increasing. Getting the most benefit from this large-scale data requires analysis of patterns it contains, which is computationally intensive or even intractable. Summarization techniques produce compact data representations (summaries) that enable faster processing by complex algorithms and queries. This talk will cover summarization of interconnected data (graphs) [3], which can represent a variety of natural processes

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Huang, Yu-Xuan, Wang-Zhou Dai, Yuan Jiang, and Zhi-Hua Zhou. "Enabling Knowledge Refinement upon New Concepts in Abductive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 7928–35. http://dx.doi.org/10.1609/aaai.v37i7.25959.

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Recently there are great efforts on leveraging machine learning and logical reasoning. Many approaches start from a given knowledge base, and then try to utilize the knowledge to help machine learning. In real practice, however, the given knowledge base can often be incomplete or even noisy, and thus, it is crucial to develop the ability of knowledge refinement or enhancement. This paper proposes to enable the Abductive learning (ABL) paradigm to have the ability of knowledge refinement/enhancement. In particular, we focus on the problem that, in contrast to closed-environment tasks where a fi

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Su, Youneng, Qing Xu, Xinming Zhu, Fubing Zhang, and Yi Liu. "Automatic Functional Classification of Buildings Supported by a POI Semantic Characterization Knowledge Graph." ISPRS International Journal of Geo-Information 13, no. 8 (2024): 285. http://dx.doi.org/10.3390/ijgi13080285.

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The division of urban functional zones is crucial for understanding urban characteristics and aiding in urban management and planning. Traditional methods, like dividing based on blocks and grids, are insufficient for modern demands. To address this, a knowledge-graph-supported method for building functional category division is proposed. Firstly, the associations between points of interest (POI) and buildings are established using triangulation and buffer zones. Then, a knowledge graph of buildings is constructed through entity and relationship extraction. A functional category classification

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15

Akshaya A M, Prem Kumar S, Sam leo A, and Dr.T. Kavitha. "Improving LLM Accuracy and Minimizing Hallucinations with Query Refinement and Knowledge Graphs." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1155–63. https://doi.org/10.47392/irjaem.2025.0189.

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Recent developments in natural language processing (NLP) using large language models (LLMs) have transformed information retrieval systems. Problems still exist, however, in high stakes use cases where high accuracy is an essential requirement. A key issue is the hallucination problem, where models generate information unsupported by the underlying data, potentially leading to dangerous misinformation. This paper introduces a new approach addressing this gap by combining large language models (LLMs) with Query Refinement Technique and knowledge graphs (KGs) to improve question-answering system

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Kim Cheng, A. M., and Hsiu-yen Tsai. "A graph-based approach for timing analysis and refinement of OPS5 knowledge-based systems." IEEE Transactions on Knowledge and Data Engineering 16, no. 2 (2004): 271–88. http://dx.doi.org/10.1109/tkde.2004.1269603.

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Zhuang, Weibin, Taihua Zhang, Liguo Yao, Yao Lu, and Panliang Yuan. "A Research on Image Semantic Refinement Recognition of Product Surface Defects Based on Causal Knowledge." Applied Sciences 12, no. 17 (2022): 8828. http://dx.doi.org/10.3390/app12178828.

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The images of surface defects of industrial products contain not only the defect type but also the causal logic related to defective design and manufacturing. This information is recessive and unstructured and difficult to find and use, which cannot provide an apriori basis for solving the problem of product defects in design and manufacturing. Therefore, in this paper, we propose an image semantic refinement recognition method based on causal knowledge for product surface defects. Firstly, an improved ResNet was designed to improve the image classification effect. Then, the causal knowledge g

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Ma, Jie, Zhitao Gao, Qi Chai, et al. "Debate on Graph: A Flexible and Reliable Reasoning Framework for Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24768–76. https://doi.org/10.1609/aaai.v39i23.34658.

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Large Language Models (LLMs) may suffer from hallucinations in real-world applications due to the lack of relevant knowledge. In contrast, knowledge graphs encompass extensive, multi-relational structures that store a vast array of symbolic facts. Consequently, integrating LLMs with knowledge graphs has been extensively explored, with Knowledge Graph Question Answering (KGQA) serving as a critical touchstone for the integration. This task requires LLMs to answer natural language questions by retrieving relevant triples from knowledge graphs. However, existing methods face two significant chall

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Trappey, Amy J. C., Ging-Bin Lin, and Li-Ping Hung. "Intelligent Text Mining for Ontological Knowledge Graph Refinement and Patent Portfolio Analysis—Case Study of Net-Zero Data Center Innovation Management." Information 15, no. 7 (2024): 374. http://dx.doi.org/10.3390/info15070374.

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Ontological knowledge graph (OKG) is a well-formed visual representation that depicts knowledge organization in formal elements (e.g., entities and attributes) and their interrelationships. OKG is crucial for innovation management analysis as it provides a clear boundary to understand complex knowledge domain in detail. In the patent analysis field, it facilitates the definition of a well-defined patent portfolio, aiming for accurate and complete patent retrievals and subsequent analyses. In recent decade, the rapid growth of the information and communication technology (ICT) sector has render

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Osipova, Irina, and Veselina Gospodinova. "Representation of the process of sudden outbursts of coal and gas using a knowledge graph." E3S Web of Conferences 192 (2020): 04022. http://dx.doi.org/10.1051/e3sconf/202019204022.

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In the process of developing a coal deposit, significant amounts of data and extensive knowledge about the object of operation are accumulated. This data and knowledge may not be structured, not reliable, contradictory. In turn, for the further conduct of the entire complex of mining operations and the development of a coal mining enterprise, structured and reliable data and knowledge are needed. The aim of the study is to propose structuring knowledge about the process of sudden outbursts of coal and gas by presenting knowledge about the subject of research in the form of an elementary knowle

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Houcemeddine, Turki, Jemielniak Dariusz, Ali Hadj Taieb Mohamed, et al. "Using logical constraints to validate statistical information about COVID-19 in collaborative knowledge graphs: the case of Wikidata." PeerJ Computer Science 8 (August 30, 2020): e1085. https://doi.org/10.5281/zenodo.7125137.

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Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our method

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Abedini, Farhad, Mohammad Reza Keyvanpour, and Mohammad Bagher Menhaj. "Correction Tower: A General Embedding Method of the Error Recognition for the Knowledge Graph Correction." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 10 (2020): 2059034. http://dx.doi.org/10.1142/s021800142059034x.

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Today, knowledge graphs (KGs) are growing by enrichment and refinement methods. The enrichment and refinement can be gained using the correction and completion of the KG. The studies of the KG completion are rich, but less attention has been paid to the methods of the KG error correction. The correction methods are divided into embedding and nonembedding methods. Embedding correction methods have been recently introduced in which a KG is embedded into a vector space. Also, existing correction approaches focused on the recognition of the three types of errors, the outliers, inconsistencies and

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Liu, Shengheng, Tianqi Zhang, Ningning Fu, and Yongming Huang. "Fine-Grained Graph Representation Learning for Heterogeneous Mobile Networks with Attentive Fusion and Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 18933–42. https://doi.org/10.1609/aaai.v39i18.34084.

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AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent network self-driving holds the key, it heavily relies on expert experience and knowledge extracted from network data. In an effort to facilitate convenient analytics and utilization of wireless big data, we introduce the concept of knowledge graphs into the field of mobile networks, giving rise to what we term as wireless data knowledge graphs (WDKGs). However, the heterogeneo

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Mohammadi, Bahram, Yicong Hong, Yuankai Qi, Qi Wu, Shirui Pan, and Javen Qinfeng Shi. "Augmented Commonsense Knowledge for Remote Object Grounding." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (2024): 4269–77. http://dx.doi.org/10.1609/aaai.v38i5.28223.

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The vision-and-language navigation (VLN) task necessitates an agent to perceive the surroundings, follow natural language instructions, and act in photo-realistic unseen environments. Most of the existing methods employ the entire image or object features to represent navigable viewpoints. However, these representations are insufficient for proper action prediction, especially for the REVERIE task, which uses concise high-level instructions, such as “Bring me the blue cushion in the master bedroom”. To address enhancing representation, we propose an augmented commonsense knowledge model (ACK)

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Zhang, Litian, Xiaoming Zhang, Ziyi Zhou, Feiran Huang, and Chaozhuo Li. "Reinforced Adaptive Knowledge Learning for Multimodal Fake News Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16777–85. http://dx.doi.org/10.1609/aaai.v38i15.29618.

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Nowadays, detecting multimodal fake news has emerged as a foremost concern since the widespread dissemination of fake news may incur adverse societal impact. Conventional methods generally focus on capturing the linguistic and visual semantics within the multimodal content, which fall short in effectively distinguishing the heightened level of meticulous fabrications. Recently, external knowledge is introduced to provide valuable background facts as complementary to facilitate news detection. Nevertheless, existing knowledge-enhanced endeavors directly incorporate all knowledge contexts throug

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Gupta, Sudhanshu, and Krishna Kumar Tiwari. "Exploratory Search Prompt Generation using n-Degree Connection in Knowledge Graph." Asian Journal of Research in Computer Science 16, no. 4 (2023): 318–26. http://dx.doi.org/10.9734/ajrcos/2023/v16i4393.

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Search engines play a vital role in retrieving in- formation, but users often struggle to express their precise information needs, resulting in less-than-optimal search results. Therefore, enhancing search query refinement is crucial to elevate the accuracy and relevance of search outcomes. One particular challenge that existing search engines face is presenting refined results for queries containing two or more unrelated entities. This paper presents a novel approach for efficient search prompt generation by leveraging connected nodes and attributes in the knowledge graph. We propose a

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Huang, Wen Tao, Pei Lu Niu, Yin Feng Liu, and Wei Jie Wang. "Spur Bevel Gearbox Fault Diagnosis Based on Wavelet Packet Transform for Feature Extraction and Flow Graph Data Mining." Advanced Materials Research 753-755 (August 2013): 2297–302. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2297.

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Gearbox vibration signal contains a wealth of the gear status information, used wavelet packet transform (WPT) refinement of the partial lock ability to extract the fault signs attribute information in the vibration signal. Extracted signs attribute information as the input of the flow graph (FG), generated decision rules to achieve the purpose of fault diagnosis. FG was a knowledge representation and data mining method to mine the intrinsic link between the data and improve the clarity of the potential knowledge. The results confirmed that used of WPT feature extraction and FG data mining met

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Li, Chunhua, Pengpeng Zhao, Victor S. Sheng, Xuefeng Xian, Jian Wu, and Zhiming Cui. "Refining Automatically Extracted Knowledge Bases Using Crowdsourcing." Computational Intelligence and Neuroscience 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/4092135.

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Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic cons

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Xu, Lin, Qixian Zhou, Ke Gong, Xiaodan Liang, Jianheng Tang, and Liang Lin. "End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7346–53. http://dx.doi.org/10.1609/aaai.v33i01.33017346.

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Beyond current conversational chatbots or task-oriented dialogue systems that have attracted increasing attention, we move forward to develop a dialogue system for automatic medical diagnosis that converses with patients to collect additional symptoms beyond their self-reports and automatically makes a diagnosis. Besides the challenges for conversational dialogue systems (e.g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations.

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Babalou, Samira, Costa David Schellenberger, Helge Bruelheide, et al. "iKNOW: A platform for knowledge graph construction for biodiversity." Biodiversity Information Science and Standards 6 (August 23, 2022): e93867. https://doi.org/10.3897/biss.6.93867.

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Nowadays, more and more biodiversity datasets containing observational and experimental data are collected and produced by different projects. In order to answer the fundamental questions of biodiversity research, these data need to be integrated for joint analyses. However, to date, too often, these data remain isolated in silos.Both in academia and industry, Knowledge Graphs (KGs) are widely regarded as a promising approach to overcome issues of data silos and lack of common understanding of data (Fensel and Şimşek 2020). KGs are graph-structured knowledge bases that store factual informatio

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Zeng, Yajian, Xiaorong Hou, Xinrui Wang, and Junying Li. "Towards a Unified Temporal and Event Logic Paradigm for Multi-Hop Path Reasoning in Knowledge Graphs." Electronics 14, no. 3 (2025): 516. https://doi.org/10.3390/electronics14030516.

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Path reasoning in knowledge graphs is a pivotal task for uncovering complex relational patterns and facilitating advanced inference processes. It also holds significant potential in domains such as power electronics, where real-time reasoning over dynamic, evolving data is essential for advancing topology design and application systems. Despite its importance, traditional approaches often encounter substantial limitations when applied to dynamic, time-sensitive scenarios. These models typically fail to adequately capture intricate logical dependencies and demonstrate suboptimal performance in

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Fan, Yan, Yu Wang, Pengfei Zhu, and Qinghua Hu. "Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 11927–35. http://dx.doi.org/10.1609/aaai.v38i11.29079.

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Continual learning (CL) has shown promising results and comparable performance to learning at once in a fully supervised manner. However, CL strategies typically require a large number of labeled samples, making their real-life deployment challenging. In this work, we focus on semi-supervised continual learning (SSCL), where the model progressively learns from partially labeled data with unknown categories. We provide a comprehensive analysis of SSCL and demonstrate that unreliable distributions of unlabeled data lead to unstable training and refinement of the progressing stages. This problem

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Pu, Bin, Xingguo Lv, Jiewen Yang, et al. "Leveraging Anatomical Consistency for Multi-Object Detection in Ultrasound Images via Source-free Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6532–40. https://doi.org/10.1609/aaai.v39i6.32700.

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Source-free unsupervised domain adaptation aims to eliminate domain shifts when data from the source domain and annotation from the target domain are not available. The multi-object detection tasks in medical image analysis are constrained by patient privacy and extremely huge annotation consumption. Hence, Source-free UDA is considered a more practical approach for eliminating the domain gap. However, relevant research that explores this topic is a dearth. In this paper, we design an Anatomy-aware Alignment Teacher-Student learning method using topological consistency based on a mean-teacher

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Kabashkin, Igor. "Development of Digital Training Twins in the Aircraft Maintenance Ecosystem." Algorithms 18, no. 7 (2025): 411. https://doi.org/10.3390/a18070411.

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This paper presents an integrated digital training twin framework for adaptive aircraft maintenance education, combining real-time competence modeling, algorithmic orchestration, and cloud–edge deployment architectures. The proposed system dynamically evaluates learner skill gaps and assigns individualized training resources through a multi-objective optimization function that balances skill alignment, Bloom’s cognitive level, fidelity tier, and time efficiency. A modular orchestration engine incorporates reinforcement learning agents for policy refinement, federated learning for privacy-prese

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Yan, Mengyi, Yaoshu Wang, Yue Wang, Xiaoye Miao, and Jianxin Li. "GIDCL: A Graph-Enhanced Interpretable Data Cleaning Framework with Large Language Models." Proceedings of the ACM on Management of Data 2, no. 6 (2024): 1–29. https://doi.org/10.1145/3698811.

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Data quality is critical across many applications. The utility of data is undermined by various errors, making rigorous data cleaning a necessity. Traditional data cleaning systems depend heavily on predefined rules and constraints, which necessitate significant domain knowledge and manual effort. Moreover, while configuration-free approaches and deep learning methods have been explored, they struggle with complex error patterns, lacking interpretability, requiring extensive feature engineering or labeled data. This paper introduces GIDCL ( G raph-enhanced I nterpretable D ata C leaning with L

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Qu, Fang, Youqiang Sun, Man Zhou, et al. "Vegetation Land Segmentation with Multi-Modal and Multi-Temporal Remote Sensing Images: A Temporal Learning Approach and a New Dataset." Remote Sensing 16, no. 1 (2023): 3. http://dx.doi.org/10.3390/rs16010003.

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In recent years, remote sensing analysis has gained significant attention in visual analysis applications, particularly in segmenting and recognizing remote sensing images. However, the existing research has predominantly focused on single-period RGB image analysis, thus overlooking the complexities of remote sensing image capture, especially in highly vegetated land parcels. In this paper, we provide a large-scale vegetation remote sensing (VRS) dataset and introduce the VRS-Seg task for multi-modal and multi-temporal vegetation segmentation. The VRS dataset incorporates diverse modalities an

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D’yachkova, Olga N., and Alexander E. Mikhailov. "Management of urban public green spaces." Stroitel'stvo: nauka i obrazovanie [Construction: Science and Education] 13, no. 1 (2023): 152–73. http://dx.doi.org/10.22227/2305-5502.2023.1.11.

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Introduction. People want effective management and balanced development of urbanised systems. In a comprehensive social, economic and environmental research of human living conditions in the city, various kinds of sociological surveys of the population are applied and foresight sessions are held with subject matter experts to analyse the existing level of safety and comfort of residence. However, in the context of growing urbanized systems, there is an acute shortage of new methods, ways and tools of knowing them for the purpose of effective management and balanced development. Materials and m

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Wang, Jianjiang, and Lingling Sun. "Impact of Personalized Learning Paths Supported by Mobile Technology on Student Academic Achievement." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 08 (2025): 174–87. https://doi.org/10.3991/ijim.v19i08.55339.

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With the rapid advancement of mobile technology, mobile learning has become an integral component of modern education, particularly in the design and implementation of personalized learning paths. These paths enable tailored learning content and strategies based on students’ interests, abilities, and progress, thereby enhancing knowledge acquisition and improving academic achievement. Recent studies on personalized learning paths have primarily focused on content recommendation and learning outcome assessment, whereas limited attention has been given to student interaction relationships. In mo

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Perera, S. N., N. Hetti Arachchige, and D. Schneider. "Integration of Image Data for Refining Building Boundaries Derived from Point Clouds." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 253–58. http://dx.doi.org/10.5194/isprsarchives-xl-3-253-2014.

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Geometrically and topologically correct 3D building models are required to satisfy with new demands such as 3D cadastre, map updating, and decision making. More attention on building reconstruction has been paid using Airborne Laser Scanning (ALS) point cloud data. The planimetric accuracy of roof outlines, including step-edges is questionable in building models derived from only point clouds. This paper presents a new approach for the detection of accurate building boundaries by merging point clouds acquired by ALS and aerial photographs. It comprises two major parts: reconstruction of initia

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Wallner, Johannes P., Andreas Niskanen, and Matti Järvisalo. "Complexity Results and Algorithms for Extension Enforcement in Abstract Argumentation." Journal of Artificial Intelligence Research 60 (September 13, 2017): 1–40. http://dx.doi.org/10.1613/jair.5415.

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Argumentation is an active area of modern artificial intelligence (AI) research, with connections to a range of fields, from computational complexity theory and knowledge representation and reasoning to philosophy and social sciences, as well as application-oriented work in domains such as legal reasoning, multi-agent systems, and decision support. Argumentation frameworks (AFs) of abstract argumentation have become the graph-based formal model of choice for many approaches to argumentation in AI, with semantics defining sets of jointly acceptable arguments, i.e., extensions. Understanding the

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Skripnik, Konstantin. "Progress in Philosophy: pro et contra in Metaphilosophical Studies." Voprosy Filosofii, no. 4 (April 2024): 97–106. http://dx.doi.org/10.21146/0042-8744-2024-4-97-106.

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The article describes the positions for and against the possibility of considering progress in philosophy in the general framework of progress in science. The works of E. Dietrich, D. Chalmers, J. Shand, representing pessimistic or moderately pessimistic points of view, and the works of D. Stoljar, T. Williamson, P.M.S. Hacker as expressing an optimistic point of view, act as supporters of the opposition. Some models of considering progress are proposed: progress in the framework of the “abscissa axis” and “ordinate axis”, a model of progress in the form of a directed graph, in the form of

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Bughio, Kulsoom S., David M. Cook, and Afaq Shah. "Investigating the intersections of vulnerability detection and Internet of Medical Things (IoMT) in healthcare, a scoping review protocol for Remote Patient Monitoring." Research, Society and Development 13, no. 6 (2024): e11313646080. http://dx.doi.org/10.33448/rsd-v13i6.46080.

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Due to the rapid and ubiquitous development and acceptance of IoT, healthcare providers have changed their locational settings from solely based in clinics to extend more broadly into the reach of patients’ domestic homes. This IoMT focus extends to various medical devices and applications within the healthcare domain, such as any form of smartphones, surveillance cameras, wearable sensors, and actuators, that hold the capability to access IoT technologies. The aim of this scoping review has two important objectives. The first is to understand the best approaches towards acquisition and refine

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Saxena, Mohit Chandra, Munish Sabharwal, and Preeti Bajaj. "Exploring Path Computation Techniques in Software-Defined Networking: A Review and Performance Evaluation of Centralized, Distributed, and Hybrid Approaches." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 553–67. http://dx.doi.org/10.17762/ijritcc.v11i9s.7468.

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Software-Defined Networking (SDN) is a networking paradigm that allows network administrators to dynamically manage network traffic flows and optimize network performance. One of the key benefits of SDN is the ability to compute and direct traffic along efficient paths through the network. In recent years, researchers have proposed various SDN-based path computation techniques to improve network performance and reduce congestion. This review paper provides a comprehensive overview of SDN-based path computation techniques, including both centralized and distributed approaches. We discuss

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Harrahill, Kieran, Áine Macken-Walsh, Eoin O’Neill, and Mick Lennon. "An Analysis of Irish Dairy Farmers’ Participation in the Bioeconomy: Exploring Power and Knowledge Dynamics in a Multi-actor EIP-AGRI Operational Group." Sustainability 14, no. 19 (2022): 12098. http://dx.doi.org/10.3390/su141912098.

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The European Commission’s European Innovation Partnership for Agricultural Productivity and Sustainability (EIP-AGRI), part of the European Commission’s Europe 2020 strategy, aims to ‘achieve more and better from less’ by bringing together a diversity of innovation actors to harness their combined knowledges to creatively achieve sustainability goals. The creation and novel use of biomaterials remains both a significant challenge and opportunity and bringing together all the relevant actors from primary production through to refinement and processing is anticipated to make progress in bringing

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Barrière, Caroline. "Hierarchical refinement and representation of the causal relation." Terminology 8, no. 1 (2002): 91–111. http://dx.doi.org/10.1075/term.8.1.05bar.

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This research looks at the complexity inherent in the causal relation and the implications for its representation in a Terminological Knowledge Base (TKB). Supported by a more general study of semantic relation hierarchies, a hierarchical refinement of the causal relation is proposed. It results from a manual search of a corpus which shows that it efficiently captures and formalizes variations expressed in text. The feasibility of determining such categorization during automatic extraction from corpora is also explored. Conceptual graphs are used as a representation formalism to which we have

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OSIPOVA, Irina. "Dynamic model construction method for coal quality control in complex-structural deposits." Sustainable Development of Mountain Territories 14, no. 4 (2022): 586–93. http://dx.doi.org/10.21177/1998-4502-2022-14-4-586-593.

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Introduction. Sustainable development of the coal mine is inextricably linked to quality control of the coal produced. Three main factors influence coal quality: geological, technological and industrial. The process of these factors influencing is varied: mean values of field quality indicators are generally known from exploration data, and during coal mining quality values may change. Keeping the required production balance in terms of the quality of coal produced is an immediate task of the coal-mining enterprise in terms of the strategy of complex development of solid mineral deposits. It s

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Khan, Muhammad Saif Ullah, Muhammad Zeshan Afzal, and Didier Stricker. "SituationalLLM: Proactive Language Models with Scene Awareness for Dynamic, Contextual Task Guidance." Open Research Europe 5 (March 3, 2025): 61. https://doi.org/10.12688/openreseurope.18551.1.

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Background Large Language Models (LLMs) have demonstrated remarkable success in text-based reasoning tasks but struggle to provide actionable guidance in real-world physical environments. This limitation arises from their lack of situational awareness—an inability to recognize gaps in their understanding of a user’s physical context, leading to unreliable and overly generic instructions. To address this, we propose SituationalLLM, a novel approach that integrates structured scene representations into LLMs to improve context-aware assistance. Methods SituationalLLM leverages scene graphs—struct

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Cai, Yuandao, and Charles Zhang. "A Cocktail Approach to Practical Call Graph Construction." Proceedings of the ACM on Programming Languages 7, OOPSLA2 (2023): 1001–33. http://dx.doi.org/10.1145/3622833.

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After decades of research, constructing call graphs for modern C-based software remains either imprecise or inefficient when scaling up to the ever-growing complexity. The main culprit is the difficulty of resolving function pointers, as precise pointer analyses are cubic in nature and become exponential when considering calling contexts. This paper takes a practical stance by first conducting a comprehensive empirical study of function pointer manipulations in the wild. By investigating 5355 indirect calls in five popular open-source systems, we conclude that, instead of the past uniform trea

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Ding, Yepeng, and Hiroyuki Sato. "Formalism-Driven Development: Concepts, Taxonomy, and Practice." Applied Sciences 12, no. 7 (2022): 3415. http://dx.doi.org/10.3390/app12073415.

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Formal methods are crucial in program specification and verification. Instead of building cases to test functionalities, formal methods specify functionalities as properties and mathematically prove them. Nevertheless, the applicability of formal methods is limited in most development processes due to the requirement of mathematical knowledge for developers. To promote the application of formal methods, we formulate formalism-driven development (FDD), which is an iterative and incremental development process that guides developers to adopt proper formal methods throughout the whole development

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Opiła, Janusz. "Role of Visualization in a Knowledge Transfer Process." Business Systems Research Journal 10, no. 1 (2019): 164–79. http://dx.doi.org/10.2478/bsrj-2019-0012.

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Abstract Background: Efficient management of the knowledge requires implementation of new tools and refinement of the old ones - one of them is visualization. As visualization turns out to be an efficient tool for transfer of acquired knowledge, understanding of the influence of visualization techniques on the process of knowledge sharing is a necessity. Objectives: The main objective of the paper is to deepen the understanding of the relation of visualization to other knowledge sharing paths. The supplementary goal is a discussion of constraints on visualization styles in relation to readabil

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