Literatura académica sobre el tema "Knowledge Graph Evaluation"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Knowledge Graph Evaluation".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Knowledge Graph Evaluation"
Gao, Junyang, Xian Li, Yifan Ethan Xu, Bunyamin Sisman, Xin Luna Dong y Jun Yang. "Efficient knowledge graph accuracy evaluation". Proceedings of the VLDB Endowment 12, n.º 11 (julio de 2019): 1679–91. http://dx.doi.org/10.14778/3342263.3342642.
Texto completoWang, Wenguang, Yonglin Xu, Chunhui Du, Yunwen Chen, Yijie Wang y Hui Wen. "Data Set and Evaluation of Automated Construction of Financial Knowledge Graph". Data Intelligence 3, n.º 3 (2021): 418–43. http://dx.doi.org/10.1162/dint_a_00108.
Texto completoAlshahrani, Mona, Maha A. Thafar y Magbubah Essack. "Application and evaluation of knowledge graph embeddings in biomedical data". PeerJ Computer Science 7 (18 de febrero de 2021): e341. http://dx.doi.org/10.7717/peerj-cs.341.
Texto completoMao, Yanmei. "Summary and Evaluation of the Application of Knowledge Graphs in Education 2007–2020". Discrete Dynamics in Nature and Society 2021 (28 de septiembre de 2021): 1–10. http://dx.doi.org/10.1155/2021/6304109.
Texto completoMalaviya, Chaitanya, Chandra Bhagavatula, Antoine Bosselut y Yejin Choi. "Commonsense Knowledge Base Completion with Structural and Semantic Context". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 03 (3 de abril de 2020): 2925–33. http://dx.doi.org/10.1609/aaai.v34i03.5684.
Texto completoSekkal, Houda, Naïla Amrous y Samir Bennani. "Knowledge graph-based method for solutions detection and evaluation in an online problem-solving community". International Journal of Electrical and Computer Engineering (IJECE) 12, n.º 6 (1 de diciembre de 2022): 6350. http://dx.doi.org/10.11591/ijece.v12i6.pp6350-6362.
Texto completoMonka, Sebastian, Lavdim Halilaj y Achim Rettinger. "A survey on visual transfer learning using knowledge graphs". Semantic Web 13, n.º 3 (6 de abril de 2022): 477–510. http://dx.doi.org/10.3233/sw-212959.
Texto completoLi, Pu, Tianci Li, Xin Wang, Suzhi Zhang, Yuncheng Jiang y Yong Tang. "Scholar Recommendation Based on High-Order Propagation of Knowledge Graphs". International Journal on Semantic Web and Information Systems 18, n.º 1 (enero de 2022): 1–19. http://dx.doi.org/10.4018/ijswis.297146.
Texto completoGrundspenkis, Janis y Maija Strautmane. "Usage of Graph Patterns for Knowledge Assessment Based on Concept Maps". Scientific Journal of Riga Technical University. Computer Sciences 38, n.º 38 (1 de enero de 2009): 60–71. http://dx.doi.org/10.2478/v10143-009-0005-y.
Texto completoZhang, Yixiao, Xiaosong Wang, Ziyue Xu, Qihang Yu, Alan Yuille y Daguang Xu. "When Radiology Report Generation Meets Knowledge Graph". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 12910–17. http://dx.doi.org/10.1609/aaai.v34i07.6989.
Texto completoTesis sobre el tema "Knowledge Graph Evaluation"
Stefanoni, Giorgio. "Evaluating conjunctive and graph queries over the EL profile of OWL 2". Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:232978e9-90a2-41cc-afd5-319518296894.
Texto completoMcNaughton, Ross. "Inference graphs : a structural model and measures for evaluating knowledge-based systems". Thesis, London South Bank University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260994.
Texto completoIssa, Subhi. "Linked data quality : completeness and conciseness". Electronic Thesis or Diss., Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1274.
Texto completoThe wide spread of Semantic Web technologies such as the Resource Description Framework (RDF) enables individuals to build their databases on the Web, to write vocabularies, and define rules to arrange and explain the relationships between data according to the Linked Data principles. As a consequence, a large amount of structured and interlinked data is being generated daily. A close examination of the quality of this data could be very critical, especially, if important research and professional decisions depend on it. The quality of Linked Data is an important aspect to indicate their fitness for use in applications. Several dimensions to assess the quality of Linked Data are identified such as accuracy, completeness, provenance, and conciseness. This thesis focuses on assessing completeness and enhancing conciseness of Linked Data. In particular, we first proposed a completeness calculation approach based on a generated schema. Indeed, as a reference schema is required to assess completeness, we proposed a mining-based approach to derive a suitable schema (i.e., a set of properties) from data. This approach distinguishes between essential properties and marginal ones to generate, for a given dataset, a conceptual schema that meets the user's expectations regarding data completeness constraints. We implemented a prototype called “LOD-CM” to illustrate the process of deriving a conceptual schema of a dataset based on the user's requirements. We further proposed an approach to discover equivalent predicates to improve the conciseness of Linked Data. This approach is based, in addition to a statistical analysis, on a deep semantic analysis of data and on learning algorithms. We argue that studying the meaning of predicates can help to improve the accuracy of results. Finally, a set of experiments was conducted on real-world datasets to evaluate our proposed approaches
Haller, Armin, Javier D. Fernández, Maulik R. Kamdar y Axel Polleres. "What are Links in Linked Open Data? A Characterization and Evaluation of Links between Knowledge Graphs on the Web". Department für Informationsverarbeitung und Prozessmanagement, WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/7193/1/20191002ePub_LOD_link_analysis.pdf.
Texto completoSeries: Working Papers on Information Systems, Information Business and Operations
Ojha, Prakhar. "Utilizing Worker Groups And Task Dependencies in Crowdsourcing". Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4265.
Texto completoLibros sobre el tema "Knowledge Graph Evaluation"
Zhang, Ningyu, Shumin Deng, Wei Hu, Meng Wang y Tianxing Wu. CCKS 2022 - Evaluation Track: 7th China Conference on Knowledge Graph and Semantic Computing Evaluations, CCKS 2022, Qinhuangdao, China, August 24-27, 2022, Revised Selected Papers. Springer, 2023.
Buscar texto completoZhang, Jiangtao, Ming Liu, Haofen Wang y Bing Qin. CCKS 2021 - Evaluation Track: 6th China Conference on Knowledge Graph and Semantic Computing, CCKS 2021, Guangzhou, China, December 25-26, 2021, Revised Selected Papers. Springer Singapore Pte. Limited, 2022.
Buscar texto completoCapítulos de libros sobre el tema "Knowledge Graph Evaluation"
Zhou, Zhangquan y Guilin Qi. "Implementation and Evaluation of a Backtracking Algorithm for Finding All Justifications in OWL 2 EL". En Linked Data and Knowledge Graph, 235–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-54025-7_21.
Texto completovan Bakel, Ruud, Teodor Aleksiev, Daniel Daza, Dimitrios Alivanistos y Michael Cochez. "Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification". En Lecture Notes in Computer Science, 107–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72308-8_8.
Texto completoWang, Jingchu, Jianyi Liu, Feiyu Chen, Teng Lu, Hua Huang y Jinmeng Zhao. "Cross-Knowledge Graph Entity Alignment via Neural Tensor Network". En Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 66–74. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_8.
Texto completoAtemezing, Ghislain Auguste. "Empirical Evaluation of a Cloud-Based Graph Database: the Case of Neptune". En Knowledge Graphs and Semantic Web, 31–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91305-2_3.
Texto completoZhang, Yuxin, Bohan Li, Han Gao, Ye Ji, Han Yang y Meng Wang. "Fine-Grained Evaluation of Knowledge Graph Embedding Models in Downstream Tasks". En Web and Big Data, 242–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_19.
Texto completoKuric, Emil, Javier D. Fernández y Olha Drozd. "Knowledge Graph Exploration: A Usability Evaluation of Query Builders for Laypeople". En Lecture Notes in Computer Science, 326–42. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33220-4_24.
Texto completoGuo, Pengfei, Zhiqing Cun, Tao Yang, Liang Yin, Wenqiang Chang y Qiang Gao. "Research on Knowledge Graph-Based Business Travel Analysis and Evaluation Methodology". En Applications of Decision Science in Management, 145–53. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2768-3_14.
Texto completoZheng, Xiuwen, Subhasis Dasgupta y Amarnath Gupta. "P2KG: Declarative Construction and Quality Evaluation of Knowledge Graph from Polystores". En New Trends in Database and Information Systems, 427–39. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42941-5_37.
Texto completoDe Donato, Renato, Martina Garofalo, Delfina Malandrino, Maria Angela Pellegrino, Andrea Petta y Vittorio Scarano. "QueDI: From Knowledge Graph Querying to Data Visualization". En Semantic Systems. In the Era of Knowledge Graphs, 70–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59833-4_5.
Texto completoHuang, Yuan-sheng, Jian-xun Qi y Jun-hua Zhou. "Method of Risk Discernment in Technological Innovation Based on Path Graph and Variable Weight Fuzzy Synthetic Evaluation". En Fuzzy Systems and Knowledge Discovery, 635–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_79.
Texto completoActas de conferencias sobre el tema "Knowledge Graph Evaluation"
Cai, Borui, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li y Jianxin Li. "Temporal Knowledge Graph Completion: A Survey". En 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.2023/734.
Texto completoJi, Yimu, Kaihang Liu, Shangdong Liu, Shuning Tang, Wan Xiao, Zhengyang Xu, Lin Hu, Yanlan Liu y Qiang Liu. "FEPF: A knowledge Fusion and Evaluation Method based on Pagerank and Feature Selection". En 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00095.
Texto completoRashid, Sabbir M., Amar K. Viswanathan, Ian Gross, Elisa Kendall y Deborah L. McGuinness. "Leveraging Semantics for Large-Scale Knowledge Graph Evaluation". En WebSci '17: ACM Web Science Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3091478.3162385.
Texto completoSun, Zhiqing, Shikhar Vashishth, Soumya Sanyal, Partha Talukdar y Yiming Yang. "A Re-evaluation of Knowledge Graph Completion Methods". En Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.489.
Texto completoPrado-Romero, Mario Alfonso y Giovanni Stilo. "GRETEL: Graph Counterfactual Explanation Evaluation Framework". En CIKM '22: The 31st ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3511808.3557608.
Texto completoKhokhlov, Igor y Leon Reznik. "Knowledge Graph in Data Quality Evaluation for IoT applications". En 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE, 2020. http://dx.doi.org/10.1109/wf-iot48130.2020.9221091.
Texto completoXu, ZhenHao, Yan Gao y Fei Yu. "Quality Evaluation Model of AI-based Knowledge Graph System". En 2021 3rd International Conference on Natural Language Processing (ICNLP). IEEE, 2021. http://dx.doi.org/10.1109/icnlp52887.2021.00018.
Texto completoFaralli, Stefano, Irene Finocchi, Simone Paolo Ponzetto y Paola Velardi. "Efficient Pruning of Large Knowledge Graphs". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/564.
Texto completoMirza, Paramita, Fariz Darari y Rahmad Mahendra. "KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents". En Proceedings of The 12th International Workshop on Semantic Evaluation. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/s18-1010.
Texto completoHalliwell, Nicholas, Fabien Gandon y Freddy Lecue. "User Scored Evaluation of Non-Unique Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs". En K-CAP '21: Knowledge Capture Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460210.3493557.
Texto completo