skip to main content
10.1145/3660043.3660092acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicieaiConference Proceedingsconference-collections
research-article

Subject competition ability evaluation based on node coverage of event knowledge graph

Published: 30 May 2024 Publication History

Abstract

According to the attributes and characteristics of event knowledge graph, this paper proposes a method based on its analysis of students' competition ability and prediction of competition results with certain reliability. This paper will collect problem data from major problem websites, treat a type of problem as an event, and extract the attributes of the problem, such as difficulty and algorithm involved, so as to build a knowledge graph. After that, we will collect the problem-solving situation of the students participating in the competition, find the corresponding event node from the event knowledge graph through the attributes of the corresponding problem, and save the algorithm label as the reflection of the students' ability. Finally, by summarizing the algorithms that have been mastered and those that have not been mastered, we will get the analysis of the students' competition ability. After that, we can compare the obtained analysis report with the algorithmic ability requirements of the competition, and obtain the predicted value of the student's achievement through a certain number of simulations.

References

[1]
Guan, S., Cheng, X., Bai, L., Zhang, F., Li, Z., Zeng, Y., Jin, X., Guo J. 2022. What is Event Knowledge Graph: A Survey IEEE Transactions on Knowledge and Data Engineering, 35, 7: 7569-7589.
[2]
Zhang, J., Li, G., Zhang, M., & Zhang, Z. 2019. Event Knowledge Graph Construction for Object Detection." IEEE Access, 7, 118989-118998.
[3]
Smith J.T., Anderson M.R. 2018. Knowledge Bias in Decision-Making: An Empirical Examination. Journal of Behavioral Decision Making, 31,1: 23-38.
[4]
Qu, L., Song, D., Lin, C. Y., Jing, F., & Zhou, G. 2020. Event Extraction and Knowledge Graph Construction with Document-Level Multi-Task Learning. In Proceedings of the 58th Annual Meeting of theAssociation for Computational Linguistics, 6428-6437.
[5]
Simon, G., Elena, D. 2019. EventKG – the hub of event knowledge on the web – and biographical timeline generation. Semantic Web. Volume Pre-press, Issue Pre-press . PP 1-32.
[6]
Yanhao, L., Wei,L. 2022. Sudden Event Prediction Based on Event Knowledge Graph. Applied Sciences., Volume12,Issue21: PP 11195-11195.
[7]
Luo, Q., Niu, Z. Y., Shi, H., Wang, M., & Gao, W. 2020. "EventExtraction and Knowledge Graph Construction from Textual Data." Journal of Information Science, 46, 1, 69-85.
[8]
Yixin, S. 2021. Construction of Event Knowledge Graph based on Semantic Analysis. Tehnički vjesnik, Volume 28, Issue 5. PP 1640-1646.
[9]
YaDav,P., Salwala,D., Dibya,P.,Curry, E. 2020. Knowledge Graph Driven Approach to Represent Video Streams for Spatiotemporal Event Pattern Matching in Complex Event Processing. International Journal of Semantic Computing. Volume 14, Issue 3 . PP 33.
[10]
Zhihua,T., Xijin,T. 2023. Narrative Graph: Telling Evolving Stories Based on Event-centric Temporal Knowledge Graph. Journal of Systems Science and Systems Engineering, Volume 32, Issue 2. PP 206-221.
[11]
Li,L.,Weichao,Y. 2020. Dynamic uncertain causality graph based on Intuitionistic fuzzy sets and its application to root cause analysis. Applied Intelligence: The International Journal of Research on Intelligent Systems for Real Life Complex Problems, Volume 50, Issue 9. PP 241-255

Index Terms

  1. Subject competition ability evaluation based on node coverage of event knowledge graph

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      ICIEAI '23: Proceedings of the 2023 International Conference on Information Education and Artificial Intelligence
      December 2023
      1132 pages
      ISBN:9798400716157
      DOI:10.1145/3660043
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 30 May 2024

      Permissions

      Request permissions for this article.

      Check for updates

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Conference

      ICIEAI 2023

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 14
        Total Downloads
      • Downloads (Last 12 months)14
      • Downloads (Last 6 weeks)3
      Reflects downloads up to 19 Feb 2025

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media