Recommending Scientific Videos based on Metadata Enrichment using Linked
Open Data
release_npjzb4mtgzflnarj4jdufyhslq
by
Justyna Medrek, Christian Otto, Ralph Ewerth
2019
Abstract
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.
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