Recommending Scientific Videos based on Metadata Enrichment using Linked Open Data
This work addresses the challenge of improving video recommendations for educational and scientific content in portals like TIB AV-Portal, but it is incremental as it builds on existing metadata enrichment techniques.
The paper tackled the problem of recommending scientific videos by enriching metadata with Linked Open Data, specifically linking automatically generated video information to the Integrated Authority File (GND) to compute similarity measures for recommendations, and a user study demonstrated the feasibility of this approach.
The amount of available videos in the Web has significantly increased not only for entertainment etc., but also to convey educational or scientific information in an effective way. There are several web portals that offer access to the latter kind of video material. One of them is the TIB AV-Portal of the Leibniz Information Centre for Science and Technology (TIB), which hosts scientific and educational video content. In contrast to other video portals, automatic audiovisual analysis (visual concept classification, optical character recognition, speech recognition) is utilized to enhance metadata information and semantic search. In this paper, we propose to further exploit and enrich this automatically generated information by linking it to the Integrated Authority File (GND) of the German National Library. This information is used to derive a measure to compare the similarity of two videos which serves as a basis for recommending semantically similar videos. A user study demonstrates the feasibility of the proposed approach.