Gotowa bibliografia na temat „Knowledge Graph (KG)”
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Artykuły w czasopismach na temat "Knowledge Graph (KG)"
Hao, Wu, Jiao Menglin, Tian Guohui, Ma Qing i Liu Guoliang. "R-KG: A Novel Method for Implementing a Robot Intelligent Service". AI 1, nr 1 (2.03.2020): 117–40. http://dx.doi.org/10.3390/ai1010006.
Pełny tekst źródłaKhan, Arijit. "Knowledge Graphs Querying". ACM SIGMOD Record 52, nr 2 (10.08.2023): 18–29. http://dx.doi.org/10.1145/3615952.3615956.
Pełny tekst źródłaBai, Liting, Lin Liu, Shengli Song i Yueshen Xu. "NCR-KG: news community recommendation with knowledge graph". CCF Transactions on Pervasive Computing and Interaction 1, nr 4 (11.11.2019): 250–59. http://dx.doi.org/10.1007/s42486-019-00020-3.
Pełny tekst źródłaFang, Yin, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan i Huajun Chen. "Molecular Contrastive Learning with Chemical Element Knowledge Graph". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 4 (28.06.2022): 3968–76. http://dx.doi.org/10.1609/aaai.v36i4.20313.
Pełny tekst źródłaTong, Peihao, Qifan Zhang i Junjie Yao. "Leveraging Domain Context for Question Answering Over Knowledge Graph". Data Science and Engineering 4, nr 4 (4.11.2019): 323–35. http://dx.doi.org/10.1007/s41019-019-00109-w.
Pełny tekst źródłaTian, Xin, i Yuan Meng. "Relgraph: A Multi-Relational Graph Neural Network Framework for Knowledge Graph Reasoning Based on Relation Graph". Applied Sciences 14, nr 7 (8.04.2024): 3122. http://dx.doi.org/10.3390/app14073122.
Pełny tekst źródłaYan, Yuchen, Lihui Liu, Yikun Ban, Baoyu Jing i Hanghang Tong. "Dynamic Knowledge Graph Alignment". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 5 (18.05.2021): 4564–72. http://dx.doi.org/10.1609/aaai.v35i5.16585.
Pełny tekst źródłaKejriwal, Mayank. "Knowledge Graphs: A Practical Review of the Research Landscape". Information 13, nr 4 (23.03.2022): 161. http://dx.doi.org/10.3390/info13040161.
Pełny tekst źródłaZuo, H., Y. Yin i P. Childs. "Patent-KG: Patent Knowledge Graph Extraction for Engineering Design". Proceedings of the Design Society 2 (maj 2022): 821–30. http://dx.doi.org/10.1017/pds.2022.84.
Pełny tekst źródłaBellomarini, Luigi, Marco Benedetti, Andrea Gentili, Davide Magnanimi i Emanuel Sallinger. "KG-Roar: Interactive Datalog-Based Reasoning on Virtual Knowledge Graphs". Proceedings of the VLDB Endowment 16, nr 12 (sierpień 2023): 4014–17. http://dx.doi.org/10.14778/3611540.3611609.
Pełny tekst źródłaRozprawy doktorskie na temat "Knowledge Graph (KG)"
Salehpour, Masoud. "High-performance Query Processing over Knowledge Graphs". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28569.
Pełny tekst źródłaSima, Xingyu. "La gestion des connaissances dans les petites et moyennes entreprises : un cadre adapté et complet". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP047.
Pełny tekst źródłaKnowledge is vital for organizations, particularly in today’s Industry 4.0 context. Knowledge Management (KM) plays a critical role in an organization's success. Although KM has been relatively well-studied in large organizations, Small and Medium-sized Enterprises (SMEs) receive less attention. SMEs face unique challenges in KM, requiring a tailored KM framework. Our study aims to define a framework addressing their challenges while leveraging their inherent strengths. This thesis presents a dedicated and comprehensive SME KM framework, offering dedicated solutions from knowledge acquisition and representation to exploitation: (1) a dedicated knowledge acquisition process based on the Scrum framework, an agile methodology, (2) a dedicated knowledge representation model based on semi-structured KG, and (3) a dedicated knowledge exploitation process based on knowledge-relatedness RS. This research was conducted in collaboration with Axsens-bte, an SME specializing in consultancy and training. The partnership with Axsens-bte has provided invaluable insights and practical experiences, contributing to developing the proposed KM framework and highlighting its relevance and applicability in real-world SME contexts
Saxena, Apoorv Umang. "Leveraging KG Embeddings for Knowledge Graph Question Answering". Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6082.
Pełny tekst źródłaOjha, Prakhar. "Utilizing Worker Groups And Task Dependencies in Crowdsourcing". Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4265.
Pełny tekst źródłaCzęści książek na temat "Knowledge Graph (KG)"
Krause, Franz, Kabul Kurniawan, Elmar Kiesling, Jorge Martinez-Gil, Thomas Hoch, Mario Pichler, Bernhard Heinzl i Bernhard Moser. "Leveraging Semantic Representations via Knowledge Graph Embeddings". W Artificial Intelligence in Manufacturing, 71–85. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_5.
Pełny tekst źródłaSanou, Gaoussou, Véronique Giudicelli, Nika Abdollahi, Sofia Kossida, Konstantin Todorov i Patrice Duroux. "IMGT-KG: A Knowledge Graph for Immunogenetics". W The Semantic Web – ISWC 2022, 628–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19433-7_36.
Pełny tekst źródłaWu, Tianxing, Cong Gao, Guilin Qi, Lei Zhang, Chuanqi Dong, He Liu i Du Zhang. "KG-Buddhism: The Chinese Knowledge Graph on Buddhism". W Semantic Technology, 259–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70682-5_17.
Pełny tekst źródłaMöller, Cedric. "Knowledge Graph Population with Out-of-KG Entities". W The Semantic Web: ESWC 2022 Satellite Events, 199–214. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11609-4_35.
Pełny tekst źródłaKwapong, Benjamin, Amartya Sen i Kenneth K. Fletcher. "ELECTRA-KG: A Transformer-Knowledge Graph Recommender System". W Services Computing – SCC 2022, 56–70. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23515-3_5.
Pełny tekst źródłaPflueger, Maximilian, David J. Tena Cucala i Egor V. Kostylev. "GNNQ: A Neuro-Symbolic Approach to Query Answering over Incomplete Knowledge Graphs". W The Semantic Web – ISWC 2022, 481–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19433-7_28.
Pełny tekst źródłaMotger, Quim, Xavier Franch i Jordi Marco. "MApp-KG: Mobile App Knowledge Graph for Document-Based Feature Knowledge Generation". W Lecture Notes in Business Information Processing, 129–37. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61000-4_15.
Pełny tekst źródłaMeyer, Lars-Peter, Claus Stadler, Johannes Frey, Norman Radtke, Kurt Junghanns, Roy Meissner, Gordian Dziwis, Kirill Bulert i Michael Martin. "LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT". W Informatik aktuell, 103–15. Wiesbaden: Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-43705-3_8.
Pełny tekst źródłaDessì, Danilo, Francesco Osborne, Diego Reforgiato Recupero, Davide Buscaldi, Enrico Motta i Harald Sack. "AI-KG: An Automatically Generated Knowledge Graph of Artificial Intelligence". W Lecture Notes in Computer Science, 127–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62466-8_9.
Pełny tekst źródłaAnand, Avinash, Mohit Gupta, Kritarth Prasad, Ujjwal Goel, Naman Lal, Astha Verma i Rajiv Ratn Shah. "KG-CTG: Citation Generation Through Knowledge Graph-Guided Large Language Models". W Big Data and Artificial Intelligence, 37–49. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49601-1_3.
Pełny tekst źródłaStreszczenia konferencji na temat "Knowledge Graph (KG)"
Wei, Xing, i Jiangjiang Liu. "Effects of Nonlinear Functions on Knowledge Graph Convolutional Networks for Recommender Systems with Yelp Knowledge Graph". W 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110715.
Pełny tekst źródłaRistoski, Petar, Zhizhong Lin i Qunzhi Zhou. "KG-ZESHEL: Knowledge Graph-Enhanced Zero-Shot Entity Linking". W K-CAP '21: Knowledge Capture Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460210.3493549.
Pełny tekst źródłaChen, Mingyang, Wen Zhang, Zhen Yao, Xiangnan Chen, Mengxiao Ding, Fei Huang i Huajun Chen. "Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting". W Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/273.
Pełny tekst źródłaWei, Jiaqi, Shuo Han i Lei Zou. "VISION-KG: Topic-centric Visualization System for Summarizing Knowledge Graph". W WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3336191.3371863.
Pełny tekst źródłaQiu, Yuchen, Yuanyuan Qiao, Shuo Yang i Jie Yang. "Tax-KG: Taxation Big Data Visualization System for Knowledge Graph". W 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). IEEE, 2020. http://dx.doi.org/10.1109/icsip49896.2020.9339403.
Pełny tekst źródłaCai, Jinglun, Mingda Li, Ziyan Jiang, Eunah Cho, Zheng Chen, Yang Liu, Xing Fan i Chenlei Guo. "KG-ECO: Knowledge Graph Enhanced Entity Correction For Query Rewriting". W ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096826.
Pełny tekst źródłaLiu, Shuwen, Bernardo Cuenca Grau, Ian Horrocks i Egor V. Kostylev. "Revisiting Inferential Benchmarks for Knowledge Graph Completion". W 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/45.
Pełny tekst źródłaHuang, Yu-Xuan, Zequn Sun, Guangyao Li, Xiaobin Tian, Wang-Zhou Dai, Wei Hu, Yuan Jiang i Zhi-Hua Zhou. "Enabling Abductive Learning to Exploit Knowledge Graph". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/427.
Pełny tekst źródłaWu, Zhanglin, Min Zhang, Ming Zhu, Yinglu Li, Ting Zhu, Hao Yang, Song Peng i Ying Qin. "KG-BERTScore: Incorporating Knowledge Graph into BERTScore for Reference-Free Machine Translation Evaluation". W IJCKG 2022: 11th International Joint Conference On Knowledge Graphs. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3579051.3579065.
Pełny tekst źródłaTu, Yamei, Rui Qiu i Han-Wei Shen. "KG-PRE-view: Democratizing a TVCG Knowledge Graph through Visual Explorations". W 2024 IEEE 17th Pacific Visualization Conference (PacificVis). IEEE, 2024. http://dx.doi.org/10.1109/pacificvis60374.2024.00026.
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