Scholar metadata and knowledge generation with human and artificial intelligence

Abstract

Scholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata (structural keywords) using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars (both as authors and users) can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model. Structural

How do Students Perceive Artificial Intelligence in YouTube Educational Videos Selection? A Case Study of Al Ain City

“…X. Liu and their colleagues [28] described four systems used by Artificial Intelligence to create metadata and add it to the images, text, video, and other files. For example, search engines now categorize files like MP4, MP3, PNG, JPEG, etc.…”

An interactive metadata model for structural, descriptive, and referential representation of scholarly output

“… Structural knowledge map for the information retrieval domain (Liu, Zhang, & Guo, ). [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]…”

“…This social media platform is integrated with machine‐learning algorithms and statistical models to allow large‐scale automatic metadata generation at a low cost. Meanwhile, the user's participation and contribution help improve the performance of the machine‐learning classifier (Liu, Zhang & Guo ). In editing a metadata value, for example, a user can simply click “Edit” (see Figure ) to modify a publication's referential metadata (not citation) by using a template shown in Figure .…”