Auswahl der wissenschaftlichen Literatur zum Thema „Knowledge Graph (KG)“
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Zeitschriftenartikel zum Thema "Knowledge Graph (KG)"
Hao, Wu, Jiao Menglin, Tian Guohui, Ma Qing und Liu Guoliang. „R-KG: A Novel Method for Implementing a Robot Intelligent Service“. AI 1, Nr. 1 (02.03.2020): 117–40. http://dx.doi.org/10.3390/ai1010006.
Der volle Inhalt der QuelleKhan, Arijit. „Knowledge Graphs Querying“. ACM SIGMOD Record 52, Nr. 2 (10.08.2023): 18–29. http://dx.doi.org/10.1145/3615952.3615956.
Der volle Inhalt der QuelleBai, Liting, Lin Liu, Shengli Song und 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.
Der volle Inhalt der QuelleFang, Yin, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan und 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.
Der volle Inhalt der QuelleTong, Peihao, Qifan Zhang und Junjie Yao. „Leveraging Domain Context for Question Answering Over Knowledge Graph“. Data Science and Engineering 4, Nr. 4 (04.11.2019): 323–35. http://dx.doi.org/10.1007/s41019-019-00109-w.
Der volle Inhalt der QuelleTian, Xin, und Yuan Meng. „Relgraph: A Multi-Relational Graph Neural Network Framework for Knowledge Graph Reasoning Based on Relation Graph“. Applied Sciences 14, Nr. 7 (08.04.2024): 3122. http://dx.doi.org/10.3390/app14073122.
Der volle Inhalt der QuelleYan, Yuchen, Lihui Liu, Yikun Ban, Baoyu Jing und 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.
Der volle Inhalt der QuelleKejriwal, 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.
Der volle Inhalt der QuelleZuo, H., Y. Yin und P. Childs. „Patent-KG: Patent Knowledge Graph Extraction for Engineering Design“. Proceedings of the Design Society 2 (Mai 2022): 821–30. http://dx.doi.org/10.1017/pds.2022.84.
Der volle Inhalt der QuelleBellomarini, Luigi, Marco Benedetti, Andrea Gentili, Davide Magnanimi und Emanuel Sallinger. „KG-Roar: Interactive Datalog-Based Reasoning on Virtual Knowledge Graphs“. Proceedings of the VLDB Endowment 16, Nr. 12 (August 2023): 4014–17. http://dx.doi.org/10.14778/3611540.3611609.
Der volle Inhalt der QuelleDissertationen zum Thema "Knowledge Graph (KG)"
Salehpour, Masoud. „High-performance Query Processing over Knowledge Graphs“. Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28569.
Der volle Inhalt der QuelleSima, 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.
Der volle Inhalt der QuelleKnowledge 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.
Der volle Inhalt der QuelleOjha, Prakhar. „Utilizing Worker Groups And Task Dependencies in Crowdsourcing“. Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4265.
Der volle Inhalt der QuelleBuchteile zum Thema "Knowledge Graph (KG)"
Krause, Franz, Kabul Kurniawan, Elmar Kiesling, Jorge Martinez-Gil, Thomas Hoch, Mario Pichler, Bernhard Heinzl und Bernhard Moser. „Leveraging Semantic Representations via Knowledge Graph Embeddings“. In Artificial Intelligence in Manufacturing, 71–85. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_5.
Der volle Inhalt der QuelleSanou, Gaoussou, Véronique Giudicelli, Nika Abdollahi, Sofia Kossida, Konstantin Todorov und Patrice Duroux. „IMGT-KG: A Knowledge Graph for Immunogenetics“. In The Semantic Web – ISWC 2022, 628–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19433-7_36.
Der volle Inhalt der QuelleWu, Tianxing, Cong Gao, Guilin Qi, Lei Zhang, Chuanqi Dong, He Liu und Du Zhang. „KG-Buddhism: The Chinese Knowledge Graph on Buddhism“. In Semantic Technology, 259–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70682-5_17.
Der volle Inhalt der QuelleMöller, Cedric. „Knowledge Graph Population with Out-of-KG Entities“. In 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.
Der volle Inhalt der QuelleKwapong, Benjamin, Amartya Sen und Kenneth K. Fletcher. „ELECTRA-KG: A Transformer-Knowledge Graph Recommender System“. In Services Computing – SCC 2022, 56–70. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23515-3_5.
Der volle Inhalt der QuellePflueger, Maximilian, David J. Tena Cucala und Egor V. Kostylev. „GNNQ: A Neuro-Symbolic Approach to Query Answering over Incomplete Knowledge Graphs“. In The Semantic Web – ISWC 2022, 481–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19433-7_28.
Der volle Inhalt der QuelleMotger, Quim, Xavier Franch und Jordi Marco. „MApp-KG: Mobile App Knowledge Graph for Document-Based Feature Knowledge Generation“. In 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.
Der volle Inhalt der QuelleMeyer, Lars-Peter, Claus Stadler, Johannes Frey, Norman Radtke, Kurt Junghanns, Roy Meissner, Gordian Dziwis, Kirill Bulert und Michael Martin. „LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT“. In Informatik aktuell, 103–15. Wiesbaden: Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-43705-3_8.
Der volle Inhalt der QuelleDessì, Danilo, Francesco Osborne, Diego Reforgiato Recupero, Davide Buscaldi, Enrico Motta und Harald Sack. „AI-KG: An Automatically Generated Knowledge Graph of Artificial Intelligence“. In Lecture Notes in Computer Science, 127–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62466-8_9.
Der volle Inhalt der QuelleAnand, Avinash, Mohit Gupta, Kritarth Prasad, Ujjwal Goel, Naman Lal, Astha Verma und Rajiv Ratn Shah. „KG-CTG: Citation Generation Through Knowledge Graph-Guided Large Language Models“. In Big Data and Artificial Intelligence, 37–49. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49601-1_3.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Knowledge Graph (KG)"
Wei, Xing, und Jiangjiang Liu. „Effects of Nonlinear Functions on Knowledge Graph Convolutional Networks for Recommender Systems with Yelp Knowledge Graph“. In 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110715.
Der volle Inhalt der QuelleRistoski, Petar, Zhizhong Lin und Qunzhi Zhou. „KG-ZESHEL: Knowledge Graph-Enhanced Zero-Shot Entity Linking“. In K-CAP '21: Knowledge Capture Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460210.3493549.
Der volle Inhalt der QuelleChen, Mingyang, Wen Zhang, Zhen Yao, Xiangnan Chen, Mengxiao Ding, Fei Huang und Huajun Chen. „Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting“. In 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.
Der volle Inhalt der QuelleWei, Jiaqi, Shuo Han und Lei Zou. „VISION-KG: Topic-centric Visualization System for Summarizing Knowledge Graph“. In 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.
Der volle Inhalt der QuelleQiu, Yuchen, Yuanyuan Qiao, Shuo Yang und Jie Yang. „Tax-KG: Taxation Big Data Visualization System for Knowledge Graph“. In 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). IEEE, 2020. http://dx.doi.org/10.1109/icsip49896.2020.9339403.
Der volle Inhalt der QuelleCai, Jinglun, Mingda Li, Ziyan Jiang, Eunah Cho, Zheng Chen, Yang Liu, Xing Fan und Chenlei Guo. „KG-ECO: Knowledge Graph Enhanced Entity Correction For Query Rewriting“. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096826.
Der volle Inhalt der QuelleLiu, Shuwen, Bernardo Cuenca Grau, Ian Horrocks und Egor V. Kostylev. „Revisiting Inferential Benchmarks for Knowledge Graph Completion“. In 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.
Der volle Inhalt der QuelleHuang, Yu-Xuan, Zequn Sun, Guangyao Li, Xiaobin Tian, Wang-Zhou Dai, Wei Hu, Yuan Jiang und Zhi-Hua Zhou. „Enabling Abductive Learning to Exploit Knowledge Graph“. In 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.
Der volle Inhalt der QuelleWu, Zhanglin, Min Zhang, Ming Zhu, Yinglu Li, Ting Zhu, Hao Yang, Song Peng und Ying Qin. „KG-BERTScore: Incorporating Knowledge Graph into BERTScore for Reference-Free Machine Translation Evaluation“. In IJCKG 2022: 11th International Joint Conference On Knowledge Graphs. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3579051.3579065.
Der volle Inhalt der QuelleTu, Yamei, Rui Qiu und Han-Wei Shen. „KG-PRE-view: Democratizing a TVCG Knowledge Graph through Visual Explorations“. In 2024 IEEE 17th Pacific Visualization Conference (PacificVis). IEEE, 2024. http://dx.doi.org/10.1109/pacificvis60374.2024.00026.
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