Добірка наукової літератури з теми "Knowledge Graph Evaluation"
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Статті в журналах з теми "Knowledge Graph Evaluation"
Gao, Junyang, Xian Li, Yifan Ethan Xu, Bunyamin Sisman, Xin Luna Dong, and Jun Yang. "Efficient knowledge graph accuracy evaluation." Proceedings of the VLDB Endowment 12, no. 11 (July 2019): 1679–91. http://dx.doi.org/10.14778/3342263.3342642.
Повний текст джерелаWang, Wenguang, Yonglin Xu, Chunhui Du, Yunwen Chen, Yijie Wang, and Hui Wen. "Data Set and Evaluation of Automated Construction of Financial Knowledge Graph." Data Intelligence 3, no. 3 (2021): 418–43. http://dx.doi.org/10.1162/dint_a_00108.
Повний текст джерелаAlshahrani, Mona, Maha A. Thafar, and Magbubah Essack. "Application and evaluation of knowledge graph embeddings in biomedical data." PeerJ Computer Science 7 (February 18, 2021): e341. http://dx.doi.org/10.7717/peerj-cs.341.
Повний текст джерелаMao, Yanmei. "Summary and Evaluation of the Application of Knowledge Graphs in Education 2007–2020." Discrete Dynamics in Nature and Society 2021 (September 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/6304109.
Повний текст джерелаMalaviya, Chaitanya, Chandra Bhagavatula, Antoine Bosselut, and Yejin Choi. "Commonsense Knowledge Base Completion with Structural and Semantic Context." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2925–33. http://dx.doi.org/10.1609/aaai.v34i03.5684.
Повний текст джерелаSekkal, Houda, Naïla Amrous, and 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, no. 6 (December 1, 2022): 6350. http://dx.doi.org/10.11591/ijece.v12i6.pp6350-6362.
Повний текст джерелаMonka, Sebastian, Lavdim Halilaj, and Achim Rettinger. "A survey on visual transfer learning using knowledge graphs." Semantic Web 13, no. 3 (April 6, 2022): 477–510. http://dx.doi.org/10.3233/sw-212959.
Повний текст джерелаLi, Pu, Tianci Li, Xin Wang, Suzhi Zhang, Yuncheng Jiang, and Yong Tang. "Scholar Recommendation Based on High-Order Propagation of Knowledge Graphs." International Journal on Semantic Web and Information Systems 18, no. 1 (January 2022): 1–19. http://dx.doi.org/10.4018/ijswis.297146.
Повний текст джерелаGrundspenkis, Janis, and Maija Strautmane. "Usage of Graph Patterns for Knowledge Assessment Based on Concept Maps." Scientific Journal of Riga Technical University. Computer Sciences 38, no. 38 (January 1, 2009): 60–71. http://dx.doi.org/10.2478/v10143-009-0005-y.
Повний текст джерелаZhang, Yixiao, Xiaosong Wang, Ziyue Xu, Qihang Yu, Alan Yuille, and Daguang Xu. "When Radiology Report Generation Meets Knowledge Graph." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12910–17. http://dx.doi.org/10.1609/aaai.v34i07.6989.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаMcNaughton, 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.
Повний текст джерелаIssa, Subhi. "Linked data quality : completeness and conciseness." Electronic Thesis or Diss., Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1274.
Повний текст джерелаThe 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, and 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.
Повний текст джерелаSeries: 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.
Повний текст джерелаКниги з теми "Knowledge Graph Evaluation"
Zhang, Ningyu, Shumin Deng, Wei Hu, Meng Wang, and 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.
Знайти повний текст джерелаZhang, Jiangtao, Ming Liu, Haofen Wang, and 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.
Знайти повний текст джерелаЧастини книг з теми "Knowledge Graph Evaluation"
Zhou, Zhangquan, and Guilin Qi. "Implementation and Evaluation of a Backtracking Algorithm for Finding All Justifications in OWL 2 EL." In 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.
Повний текст джерелаvan Bakel, Ruud, Teodor Aleksiev, Daniel Daza, Dimitrios Alivanistos, and Michael Cochez. "Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification." In Lecture Notes in Computer Science, 107–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72308-8_8.
Повний текст джерелаWang, Jingchu, Jianyi Liu, Feiyu Chen, Teng Lu, Hua Huang, and Jinmeng Zhao. "Cross-Knowledge Graph Entity Alignment via Neural Tensor Network." In 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.
Повний текст джерелаAtemezing, Ghislain Auguste. "Empirical Evaluation of a Cloud-Based Graph Database: the Case of Neptune." In Knowledge Graphs and Semantic Web, 31–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91305-2_3.
Повний текст джерелаZhang, Yuxin, Bohan Li, Han Gao, Ye Ji, Han Yang, and Meng Wang. "Fine-Grained Evaluation of Knowledge Graph Embedding Models in Downstream Tasks." In Web and Big Data, 242–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_19.
Повний текст джерелаKuric, Emil, Javier D. Fernández, and Olha Drozd. "Knowledge Graph Exploration: A Usability Evaluation of Query Builders for Laypeople." In Lecture Notes in Computer Science, 326–42. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33220-4_24.
Повний текст джерелаGuo, Pengfei, Zhiqing Cun, Tao Yang, Liang Yin, Wenqiang Chang, and Qiang Gao. "Research on Knowledge Graph-Based Business Travel Analysis and Evaluation Methodology." In 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.
Повний текст джерелаZheng, Xiuwen, Subhasis Dasgupta, and Amarnath Gupta. "P2KG: Declarative Construction and Quality Evaluation of Knowledge Graph from Polystores." In 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.
Повний текст джерелаDe Donato, Renato, Martina Garofalo, Delfina Malandrino, Maria Angela Pellegrino, Andrea Petta, and Vittorio Scarano. "QueDI: From Knowledge Graph Querying to Data Visualization." In 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.
Повний текст джерелаHuang, Yuan-sheng, Jian-xun Qi, and Jun-hua Zhou. "Method of Risk Discernment in Technological Innovation Based on Path Graph and Variable Weight Fuzzy Synthetic Evaluation." In Fuzzy Systems and Knowledge Discovery, 635–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_79.
Повний текст джерелаТези доповідей конференцій з теми "Knowledge Graph Evaluation"
Cai, Borui, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, and Jianxin Li. "Temporal Knowledge Graph Completion: A Survey." 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.2023/734.
Повний текст джерелаJi, Yimu, Kaihang Liu, Shangdong Liu, Shuning Tang, Wan Xiao, Zhengyang Xu, Lin Hu, Yanlan Liu, and Qiang Liu. "FEPF: A knowledge Fusion and Evaluation Method based on Pagerank and Feature Selection." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00095.
Повний текст джерелаRashid, Sabbir M., Amar K. Viswanathan, Ian Gross, Elisa Kendall, and Deborah L. McGuinness. "Leveraging Semantics for Large-Scale Knowledge Graph Evaluation." In WebSci '17: ACM Web Science Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3091478.3162385.
Повний текст джерелаSun, Zhiqing, Shikhar Vashishth, Soumya Sanyal, Partha Talukdar, and Yiming Yang. "A Re-evaluation of Knowledge Graph Completion Methods." In 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.
Повний текст джерелаPrado-Romero, Mario Alfonso, and Giovanni Stilo. "GRETEL: Graph Counterfactual Explanation Evaluation Framework." In 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.
Повний текст джерелаKhokhlov, Igor, and Leon Reznik. "Knowledge Graph in Data Quality Evaluation for IoT applications." In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE, 2020. http://dx.doi.org/10.1109/wf-iot48130.2020.9221091.
Повний текст джерелаXu, ZhenHao, Yan Gao, and Fei Yu. "Quality Evaluation Model of AI-based Knowledge Graph System." In 2021 3rd International Conference on Natural Language Processing (ICNLP). IEEE, 2021. http://dx.doi.org/10.1109/icnlp52887.2021.00018.
Повний текст джерелаFaralli, Stefano, Irene Finocchi, Simone Paolo Ponzetto, and Paola Velardi. "Efficient Pruning of Large Knowledge Graphs." In 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.
Повний текст джерелаMirza, Paramita, Fariz Darari, and Rahmad Mahendra. "KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents." In 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.
Повний текст джерелаHalliwell, Nicholas, Fabien Gandon, and Freddy Lecue. "User Scored Evaluation of Non-Unique Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs." In K-CAP '21: Knowledge Capture Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460210.3493557.
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