Gotowa bibliografia na temat „Knowledge graph refinement”
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Artykuły w czasopismach na temat "Knowledge graph refinement"
Zhang, Dehai, Menglong Cui, Yun Yang, Po Yang, Cheng Xie, Di Liu, Beibei Yu i Zhibo Chen. "Knowledge Graph-Based Image Classification Refinement". IEEE Access 7 (2019): 57678–90. http://dx.doi.org/10.1109/access.2019.2912627.
Pełny tekst źródłaHsueh, Huei-Chia, Shuo-Chen Chien, Chih-Wei Huang, Hsuan-Chia Yang, Usman Iqbal, Li-Fong Lin i Wen-Shan Jian. "A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications". PLOS ONE 19, nr 1 (31.01.2024): e0296939. http://dx.doi.org/10.1371/journal.pone.0296939.
Pełny tekst źródłaKayali, Moe, i Dan Suciu. "Quasi-Stable Coloring for Graph Compression". Proceedings of the VLDB Endowment 16, nr 4 (grudzień 2022): 803–15. http://dx.doi.org/10.14778/3574245.3574264.
Pełny tekst źródłaPaulheim, Heiko. "Knowledge graph refinement: A survey of approaches and evaluation methods". Semantic Web 8, nr 3 (6.12.2016): 489–508. http://dx.doi.org/10.3233/sw-160218.
Pełny tekst źródłaZhang, Yichong, i Yongtao Hao. "Traditional Chinese Medicine Knowledge Graph Construction Based on Large Language Models". Electronics 13, nr 7 (7.04.2024): 1395. http://dx.doi.org/10.3390/electronics13071395.
Pełny tekst źródłaAldughayfiq, Bader, Farzeen Ashfaq, N. Z. Jhanjhi i Mamoona Humayun. "Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB". Healthcare 11, nr 12 (15.06.2023): 1762. http://dx.doi.org/10.3390/healthcare11121762.
Pełny tekst źródłaDong, Qian, Shuzi Niu, Tao Yuan i Yucheng Li. "Disentangled Graph Recurrent Network for Document Ranking". Data Science and Engineering 7, nr 1 (15.02.2022): 30–43. http://dx.doi.org/10.1007/s41019-022-00179-3.
Pełny tekst źródłaFauceglia, Nicolas, Mustafa Canim, Alfio Gliozzo, Jennifer J. Liang, Nancy Xin Ru Wang, Douglas Burdick, Nandana Mihindukulasooriya i in. "KAAPA: Knowledge Aware Answers from PDF Analysis". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 18 (18.05.2021): 16029–31. http://dx.doi.org/10.1609/aaai.v35i18.18002.
Pełny tekst źródłaKoutra, Danai. "The power of summarization in graph mining and learning". Proceedings of the VLDB Endowment 14, nr 13 (wrzesień 2021): 3416. http://dx.doi.org/10.14778/3484224.3484238.
Pełny tekst źródłaHuang, Yu-Xuan, Wang-Zhou Dai, Yuan Jiang i Zhi-Hua Zhou. "Enabling Knowledge Refinement upon New Concepts in Abductive Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 7 (26.06.2023): 7928–35. http://dx.doi.org/10.1609/aaai.v37i7.25959.
Pełny tekst źródłaRozprawy doktorskie na temat "Knowledge graph refinement"
Khajeh, Nassiri Armita. "Expressive Rule Discovery for Knowledge Graph Refinement". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG045.
Pełny tekst źródłaKnowledge graphs (KGs) are heterogeneous graph structures representing facts in a machine-readable format. They find applications in tasks such as question answering, disambiguation, and entity linking. However, KGs are inherently incomplete, and refining them is crucial to improve their effectiveness in downstream tasks. It's possible to complete the KGs by predicting missing links within a knowledge graph or integrating external sources and KGs. By extracting rules from the KG, we can leverage them to complete the graph while providing explainability. Various approaches have been proposed to mine rules efficiently. Yet, the literature lacks effective methods for effectively incorporating numerical predicates in rules. To address this gap, we propose REGNUM, which mines numerical rules with interval constraints. REGNUM builds upon the rules generated by an existing rule mining system and enriches them by incorporating numerical predicates guided by quality measures. Additionally, the interconnected nature of web data offers significant potential for completing and refining KGs, for instance, by data linking, which is the task of finding sameAs links between entities of different KGs. We introduce RE-miner, an approach that mines referring expressions (REs) for a class in a knowledge graph and uses them for data linking. REs are rules that are only applied to one entity. They support knowledge discovery and serve as an explainable way to link data. We employ pruning strategies to explore the search space efficiently, and we define characteristics to generate REs that are more relevant for data linking. Furthermore, we aim to explore the advantages and opportunities of fine-tuning language models to bridge the gap between KGs and textual data. We propose GilBERT, which leverages fine-tuning techniques on language models like BERT using a triplet loss. GilBERT demonstrates promising results for refinement tasks of relation prediction and triple classification tasks. By considering these challenges and proposing novel approaches, this thesis contributes to KG refinement, particularly emphasizing explainability and knowledge discovery. The outcomes of this research open doors to more research questions and pave the way for advancing towards more accurate and comprehensive KGs
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.
Pełny tekst źródłaMaus, 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.
Pełny tekst źródłaMelo, André [Verfasser], i 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.
Pełny tekst źródłaMelo, André Verfasser], i 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.
Pełny tekst źródłaCzęści książek na temat "Knowledge graph refinement"
Cui, Jie, Fei Pu i Bailin Yang. "Dual-Dimensional Refinement of Knowledge Graph Embedding Representation". W Knowledge Science, Engineering and Management, 124–37. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40283-8_12.
Pełny tekst źródłaLiu, Yifan, Bin Shang, Chenxin Wang i Yinliang Zhao. "Knowledge Graph Completion with Information Adaptation and Refinement". W Advanced Data Mining and Applications, 16–31. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_2.
Pełny tekst źródłaEbeid, Islam Akef, Majdi Hassan, Tingyi Wanyan, Jack Roper, Abhik Seal i Ying Ding. "Biomedical Knowledge Graph Refinement and Completion Using Graph Representation Learning and Top-K Similarity Measure". W Diversity, Divergence, Dialogue, 112–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71292-1_10.
Pełny tekst źródłaChen, Chen, Yufei Wang, Yang Zhang, Quan Z. Sheng i Kwok-Yan Lam. "Separate-and-Aggregate: A Transformer-Based Patch Refinement Model for Knowledge Graph Completion". W Advanced Data Mining and Applications, 62–77. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_5.
Pełny tekst źródłaHogan, Aidan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs, Roberto Navigli i in. "Refinement". W Knowledge Graphs, 127–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01918-0_8.
Pełny tekst źródłaYoo, Illhoi, i Xiaohua Hu. "Clustering Large Collection of Biomedical Literature Based on Ontology-Enriched Bipartite Graph Representation and Mutual Refinement Strategy". W Advances in Knowledge Discovery and Data Mining, 303–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_36.
Pełny tekst źródłaKosa, Victoria, Oles Dobosevych i Vadim Ermolayev. "Terminology Saturation Analysis: Refinements and Applications". W AI, Data, and Digitalization, 25–41. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53770-7_3.
Pełny tekst źródłaBuhl, Dominik, Daniel Szafarski, Laslo Welz i Carsten Lanquillon. "Conversation-Driven Refinement of Knowledge Graphs: True Active Learning with Humans in the Chatbot Application Loop". W Artificial Intelligence in HCI, 41–54. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35894-4_3.
Pełny tekst źródłaSchürmann, Felix, Jean-Denis Courcol i Srikanth Ramaswamy. "Computational Concepts for Reconstructing and Simulating Brain Tissue". W Advances in Experimental Medicine and Biology, 237–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89439-9_10.
Pełny tekst źródłaSchürmann, Felix, Jean-Denis Courcol i Srikanth Ramaswamy. "Computational Concepts for Reconstructing and Simulating Brain Tissue". W Advances in Experimental Medicine and Biology, 237–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89439-9_10.
Pełny tekst źródłaStreszczenia konferencji na temat "Knowledge graph refinement"
Zheng, Liu. "A Novel Graph-Based Image Annotation Refinement Algorithm". W 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.369.
Pełny tekst źródłaWu, Jiaying, i Bryan Hooi. "DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection". W KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599298.
Pełny tekst źródłaSaeedizade, Mohammad Javad, Najmeh Torabian i Behrouz Minaei-Bidgoli. "KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods". W Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.sustainlp-1.3.
Pełny tekst źródłaZhang, Qingheng, Zequn Sun, Wei Hu, Muhao Chen, Lingbing Guo i Yuzhong Qu. "Multi-view Knowledge Graph Embedding for Entity Alignment". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/754.
Pełny tekst źródłaLi, Zhongyang, Xiao Ding, Ting Liu, J. Edward Hu i Benjamin Van Durme. "Guided Generation of Cause and Effect". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/502.
Pełny tekst źródłaHeyrani Nobari, Amin, Justin Rey, Suhas Kodali, Matthew Jones i Faez Ahmed. "AutoSurf: Automated Expert-Guided Meshing With Graph Neural Networks and Conformal Predictions". W 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.
Pełny tekst źródłaMinh Le, Thao, Vuong Le, Svetha Venkatesh i Truyen Tran. "Dynamic Language Binding in Relational Visual Reasoning". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/114.
Pełny tekst źródłaAmgoud, Leila, i Vivien Beuselinck. "Equivalence of Semantics in Argumentation". W 18th International Conference on Principles of Knowledge Representation and Reasoning {KR-2021}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/kr.2021/4.
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