Relevant bibliographies by topics / Knowledge graph refinement

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Knowledge graph refinement'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Knowledge graph refinement"

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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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Dissertations / Theses on the topic "Knowledge graph refinement"

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Khajeh, Nassiri Armita. "Expressive Rule Discovery for Knowledge Graph Refinement." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG045.

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Les graphes de connaissances (KG) sont des structures de graphes hétérogènes représentant des faits dans un format lisible par une machine. Ils trouvent des applications dans des tâches telles que la réponse automatique aux questions, la désambiguïsation et liaison d'entités. Cependant, les graphes de connaissances sont intrinsèquement incomplets et il est essentiel de les raffiner pour améliorer leur qualité. Pour compléter le graphe de connaissances, il est possible de prédire les liens manquants dans un graphe de connaissances ou d'intégrer des sources externes. En extrayant des règles du g
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Gad-Elrab, Mohamed Hassan Mohamed [Verfasser]. "Explainable methods for knowledge graph refinement and exploration via symbolic reasoning / Mohamed Hassan Mohamed Gad-Elrab." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1239645341/34.

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Maus, Aaron. "Formulation of Hybrid Knowledge-Based/Molecular Mechanics Potentials for Protein Structure Refinement and a Novel Graph Theoretical Protein Structure Comparison and Analysis Technique." ScholarWorks@UNO, 2019. https://scholarworks.uno.edu/td/2673.

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Proteins are the fundamental machinery that enables the functions of life. It is critical to understand them not just for basic biology, but also to enable medical advances. The field of protein structure prediction is concerned with developing computational techniques to predict protein structure and function from a protein’s amino acid sequence, encoded for directly in DNA, alone. Despite much progress since the first computational models in the late 1960’s, techniques for the prediction of protein structure still cannot reliably produce structures of high enough accuracy to enable desired a
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Melo, André [Verfasser], and Heiko [Akademischer Betreuer] Paulheim. "Automatic refinement of large-scale cross-domain knowledge graphs / André Melo ; Betreuer: Heiko Paulheim." Mannheim : Universitätsbibliothek Mannheim, 2018. http://d-nb.info/1167160584/34.

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Melo, André Verfasser], and Heiko [Akademischer Betreuer] [Paulheim. "Automatic refinement of large-scale cross-domain knowledge graphs / André Melo ; Betreuer: Heiko Paulheim." Mannheim : Universitätsbibliothek Mannheim, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:180-madoc-459801.

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Book chapters on the topic "Knowledge graph refinement"

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Cui, Jie, Fei Pu, and Bailin Yang. "Dual-Dimensional Refinement of Knowledge Graph Embedding Representation." In Knowledge Science, Engineering and Management. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40283-8_12.

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Liu, Yifan, Bin Shang, Chenxin Wang, and Yinliang Zhao. "Knowledge Graph Completion with Information Adaptation and Refinement." In Advanced Data Mining and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_2.

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Ebeid, Islam Akef, Majdi Hassan, Tingyi Wanyan, Jack Roper, Abhik Seal, and Ying Ding. "Biomedical Knowledge Graph Refinement and Completion Using Graph Representation Learning and Top-K Similarity Measure." In Diversity, Divergence, Dialogue. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71292-1_10.

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Chen, Chen, Yufei Wang, Yang Zhang, Quan Z. Sheng, and Kwok-Yan Lam. "Separate-and-Aggregate: A Transformer-Based Patch Refinement Model for Knowledge Graph Completion." In Advanced Data Mining and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_5.

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Hogan, Aidan, Claudio Gutierrez, Michael Cochcz, et al. "Refinement." In Knowledge Graphs. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01918-0_8.

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Yoo, Illhoi, and Xiaohua Hu. "Clustering Large Collection of Biomedical Literature Based on Ontology-Enriched Bipartite Graph Representation and Mutual Refinement Strategy." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_36.

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Kosa, Victoria, Oles Dobosevych, and Vadim Ermolayev. "Terminology Saturation Analysis: Refinements and Applications." In AI, Data, and Digitalization. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53770-7_3.

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AbstractIn this paper, we outline the results of our recent research on terminology saturation analysis (TSA) in subject domain-bounded textual corpora. We present the developed TSA method. We further report about the two use cases that proved the validity, efficiency, and effectiveness of TSA. Based on our experience of TSA use, we analyse the shortcomings of the method and figure out the ways to refinement and improvement. Further, we share our prognoses on how TSA could be used for: (i) generating quality datasets of minimal size for training large language models for performing better in s
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Buhl, Dominik, Daniel Szafarski, Laslo Welz, and Carsten Lanquillon. "Conversation-Driven Refinement of Knowledge Graphs: True Active Learning with Humans in the Chatbot Application Loop." In Artificial Intelligence in HCI. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35894-4_3.

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Schürmann, Felix, Jean-Denis Courcol, and Srikanth Ramaswamy. "Computational Concepts for Reconstructing and Simulating Brain Tissue." In Advances in Experimental Medicine and Biology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89439-9_10.

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AbstractIt has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts t
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Schürmann, Felix, Jean-Denis Courcol, and Srikanth Ramaswamy. "Computational Concepts for Reconstructing and Simulating Brain Tissue." In Advances in Experimental Medicine and Biology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89439-9_10.

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AbstractIt has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts t
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Conference papers on the topic "Knowledge graph refinement"

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Ovalle, Daniel, Arpan Seth, John R. Kitchin, Carl D. Laird, and Ignacio E. Grossmann. "GRAPSE: Graph-Based Retrieval Augmentation for Process Systems Engineering." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.198790.

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Large Language Models have demonstrated potential in accelerating scientific discovery, but they face challenges when making inferences in rapidly evolving and niche domains like Process Systems Engineering (PSE). To address this, we propose a Graph-based Retrieval-Augmented Generation (RAG) pipeline specifically designed for PSE papers. Our pipeline includes custom document parsing, knowledge graph construction, and refinement to enhance retrieval accuracy. We evaluate the effectiveness of our approach using an automatically generated benchmark consisting entirely of PSE-related questions. Th
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Carta, Salvatore, Alessandro Giuliani, Marco Manolo Manca, Leonardo Piano, Livio Pompianu, and Sandro Gabriele Tiddia. "Towards Knowledge Graph Refinement: Misdirected Triple Identification." In UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization. ACM, 2024. http://dx.doi.org/10.1145/3631700.3665235.

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Zheng, Liu. "A Novel Graph-Based Image Annotation Refinement Algorithm." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.369.

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Wu, Jiaying, and Bryan Hooi. "DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2023. http://dx.doi.org/10.1145/3580305.3599298.

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Saeedizade, Mohammad Javad, Najmeh Torabian, and Behrouz Minaei-Bidgoli. "KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods." In Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.sustainlp-1.3.

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Xiao, Chunjing, Shikang Pang, Wenxin Tai, Yanlong Huang, Goce Trajcevski, and Fan Zhou. "Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-level Anomaly Detection." In KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2024. http://dx.doi.org/10.1145/3637528.3672050.

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Zhang, Qingheng, Zequn Sun, Wei Hu, Muhao Chen, Lingbing Guo, and Yuzhong Qu. "Multi-view Knowledge Graph Embedding for Entity Alignment." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/754.

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We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity
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Li, Zhongyang, Xiao Ding, Ting Liu, J. Edward Hu, and Benjamin Van Durme. "Guided Generation of Cause and Effect." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/502.

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We present a conditional text generation framework that posits sentential expressions of possible causes and effects. This framework depends on two novel resources we develop in the course of this work: a very large-scale collection of English sentences expressing causal patterns (CausalBank); and a refinement over previous work on constructing large lexical causal knowledge graphs (Cause Effect Graph). Further, we extend prior work in lexically-constrained decoding to support disjunctive positive constraints. Human assessment confirms that our approach gives high-quality and diverse outputs. F
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Heyrani Nobari, Amin, Justin Rey, Suhas Kodali, Matthew Jones, and Faez Ahmed. "AutoSurf: Automated Expert-Guided Meshing With Graph Neural Networks and Conformal Predictions." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-115065.

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Abstract Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly refined meshes may ensure precision, they come with high computational costs. Similarly, adaptive remeshing techniques require multiple simulations and come at a great computational cost. This means that the meshing process is reliant upon expert knowledge and years of experience. Automating mesh generation can save significant time and effort and lead to a faster and more efficient design process. This pap
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Minh Le, Thao, Vuong Le, Svetha Venkatesh, and Truyen Tran. "Dynamic Language Binding in Relational Visual Reasoning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/114.

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We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering. Relaxing the common assumption made by current models that the object predicates pre-exist and stay static, passive to the reasoning process, we propose that these dynamic predicates expand across the domain borders to include pair-wise visual-linguistic object binding. In our method, these contextualized object links are actively found within each recurrent reasoning step without relying on
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