Student Forum
The schedule for the student forum has not yet been finalized. The timing provided here may change depending on the convenience of the global participants. The exact schedule will be announced later.
- Starts at
- Wed, Oct 16, 2024, 08:00 EDT
- Finishes at
- Fri, Aug 16, 2024, 17:00 EDT
- Moderator
- Ying-Hsang Liu
The student forum aims to provide an opportunity for master's and doctoral students to share their experiences and exchange ideas of best practices, research in progress, and findings in areas related to metadata innovation.
Moderator
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Ying-Hsang Liu
Uppsala University, Sweden
Dr Ying-Hsang Liu is a researcher at the Department of ALM at Uppsala University in Sweden. He holds a PhD in Information Science from Rutgers University. He has held academic positions in the USA, Australia, Denmark and Norway. His research lies at the intersections of knowledge organisation, interactive information retrieval and human information behaviour in various domains, including digital humanities. He has served on the editorial boards of Online Information Review and Information Processing & Management, the iSchool Digital Humanities Curriculum Committee. A recent co-edited book published by Routledge is entitled, Information and Knowledge Organisation in Digital Humanities: Global Perspectives.
Presentations
Generating Syntactic Metadata for Automatic Video Anomaly Detection: A Data Science Application
Authors: Setegn Asnakew Kasegn, Prof.Waweru Mwangi, Dr.Michael Kimwele, Dr.Surafe Lemma Abebe
Metadata is crucial for the administration and use of data. In this work, the use of recurrent neural network in deep convolutional neural network of generative adversarial networks is optimized to improve metadata quality, particularly for video anomaly detection. Conventional models frequently fall short in producing high-caliber synthetic metadata. Our recurrent neural network with deep convolutional neural network of generative adversarial networks method addresses class imbalance, increases the accuracy of anomaly identification, and enhances the production of synthetic metadata. In comparison to previous models, our model demonstrated higher AUC values and better syntactic image metadata quality generated using CUHK Avenue dataset but this is research work is in progress.
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Setegn Asnakew Kasegn
University of Gondar
I am Setegn Asnakew Kasegn, a lecturer at the University of Gondar, Ethiopia. I hold a Bachelor's degree in Information Technology and a Master's degree in Computer Science. My responsibilities include teaching, supervising research, and leading technology transfer projects. Currently, I am pursuing a PhD in IT at Jomo Kenyatta University of Agriculture and Technology (JKUAT), Kenya, with awarded a DAAD scholarship in 2021 to support my study.
Multi-modal Knowledge Organization Approach for Incorporating Intelligent Construction Metadata
Authors: Wenjing Wu, Junzhi Jia
Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. It is difficult to reuse unstructured data in the construction industry, and there is an abundance of multi-modal resources represented by images or text, the knowledge of which is difficult to be used directly for safety analysis. In this paper, we propose a metadata-based security knowledge organization model from an intelligent construction perspective. Firstly, according to the safety management needs and construction information multi-source heterogeneous characteristics, the construction safety event knowledge connotation and characteristics are analyzed. Secondly, a metadata model for intelligent construction safety information is designed. The third, the construction image feature recognition method by fusing metadata is proposed. Finally, a multi-modal knowledge service path is explored, which provides methods for data sharing and fusion in intelligent construction management.
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Wenjing Wu
Renmin University of China
Wenjing Wu is a PhD student at Renmin University of China. Her research interests include knowledge organization, deep learning, and knowledge graphs.
Author Name Disambiguation using Canonical Knowledge Bases and Heterogeneous Information Network Embedding
Author name ambiguity significantly complicates the retrieval and attribution of journal articles. To address this issue, this research proposes a synergistic approach that integrates a data-driven strategy with curated external knowledge bases. This approach aims to effectively disambiguate author names, thereby improving the reliability of metadata by ensuring that publications are correctly attributed to their true authors. Experimental results demonstrate the superiority of the proposed method over existing approaches in similar contexts, showcasing its potential to enhance the accuracy and efficiency of scholarly communication.
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Wenjia Dong
Chinese Academy of Agricultural Sciences
Wenjia Dong is a postgraduate student studying Library and Information Studies at the Chinese Academy of Agricultural Sciences. She is interested in knowledge organization and natural language processing. Currently, she is actively engaged in several research projects that aim to harness the power of machine Learning to enhance data retrieval and information management systems.
The Preliminary Study of Musical Works’ Representations Between MARC record-based Web and Data-centric Linked Data Public Access Catalogs
Authors: Li-Kung Chi, Ya-Ning Chen
The distinction between the bibliographic records representation of musical works in MARC record-based Web Public Access Catalogs and data-centric Linked Data catalogue is examined in this preliminary study. How MARC and LD resource descriptions are used to support the PAC’s representation for musical works is also examined.
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Li Kung Chi
Graduate Institute Of Library & Information Studies, National Taiwan Normal University
Li Kung's work is closely associated with library metadata, particularly in the field of music. After earning his M.L.I.S. degree from the University at Buffalo, SUNY, he joined the School of Music at Soochow University, where he played a key role in building the music library from the ground up. From 2017 to 2024, he worked at the University Library of Tunghai University, serving as an acquisitions and cataloging librarian. Currently, he is a Ph.D. student at the Graduate Institute of Library & Information Studies, National Taiwan Normal University, focusing his research on the organization of musical works' information.
Metadata Description Framework for Chinese Paper-cutting in the Context of Intangible Cultural Heritage Inheritance
Chinese paper-cutting as a valued intangible cultural heritage (ICH) lacks comprehensive metadata descriptions. This study proposes a multi-dimensional metadata framework with six dimensions: basic attributes, connotative features, creators, ICH projects, inheritors, and derivative innovations. It incorporates elements from Dublin Core, CDWA Lite, and VRA Core for basic attributes, connotative features, and creators, and designs attributes for ICH projects and inheritors based on Chinese ICH project declarations. The derivative innovation dimension covers derived works. Usability of the proposed framework was verified using RDF metadata for "Flower Cutting Maiden". The study notes limitations in validation data and connotative feature depth, and puts forward semantic and linked data direction for the further future research.
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Shengnan Zhao
Beijing Normal University
I am a graduate student in library science at the School of Government, Beijing Normal University. My research focuses on information organization, digital literacy and AI literacy.