Mr. Tianqi Wan Profile

Tianqi Wan

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Proceedings Article | 11 October 2024 Paper

Knowledge graph error detection with unsupervised triplet network

Tianqi Wan, Faren Song, Xu Zheng

Proceedings Volume 13278, 132781A (2024) https://doi.org/10.1117/12.3032265

KEYWORDS: Error analysis, Information fusion, Data modeling, Matrices

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Knowledge Graphs (KGs) are essential in various application areas. However, their quality is often compromised due to a large number of errors. Existing KG error detection methods have limitations in versatility and practicality. They mainly rely on supervision information such as entity types or error labels, which is not always easy to obtain in real scenarios. To address this issue, the paper proposes SymNet , an innovative framework for efficiently detecting errors in KG. SymNet utilises a triple embedding strategy, treating each triple as a node, and constructs a dynamic triple network through relational symmetric triples. To overcome the challenges posed by the special data characteristics and label scarcity of KG, a multi-layer information integration design is introduced. The BiLSTM module captures local-level intra-triplet translation information, while the graph attention network collects global-level inter-triplet contextual information. This multi-layer information integration strategy enables SymNet to more comprehensively understand the associated information in the knowledge graph, resulting in a significant improvement in error detection accuracy. The experimental results demonstrate that SymNet outperforms current state-of-the-art error detection algorithms in terms of both accuracy and efficiency when tested on two real-world knowledge graphs. Our research offers novel concepts and efficient solutions for addressing error detection issues in knowledge graphs, providing robust backing for enhancing the quality of knowledge graphs in practical scenarios.

Proceedings Article | 8 November 2023 Paper

Introducing test theory to deep neural network testing: reliability, validity, difficulty and discrimination

Yifei Liu, Yinxiao Miao, Ping Yang, Tianqi Wan, Long Zhang

Proceedings Volume 12923, 1292318 (2023) https://doi.org/10.1117/12.3011360

KEYWORDS: Reliability, Neural networks, Neurons, Design and modelling, Analytical research, Data modeling, Machine learning, Testing and analysis

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Researchers have developed various test methods and tools to ensure the performance and security of deep neural network (DNN) applications and detect potential defects as much as possible. However, there is still a lack of a mature and comprehensive DNN test theory, leading to the uneven quality of test tools and inconsistent evaluation methods, which pose challenges for the selection and application of test tools. Test Theory (TT) has developed a series of mature theoretical approaches and has validated its usability over a long history and in a wide range of application scenarios. This paper analyses the DNN test process, defines the concept of DNN test tools, and surveys the current research status and limitations of the DNN test field. Inspired by TT, this paper systematically introduces TT into DNN testing for the first time building the theoretical model of reliability, validity, difficulty, and discrimination that conform to the characteristics of DNN, which provides a new research perspective for DNN testing and is a preliminary study for establishing DNN testing theory.

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