Recommending Scientific Videos based on Metadata Enrichment using Linked Open Data release_npjzb4mtgzflnarj4jdufyhslq

by Justyna Medrek, Christian Otto, Ralph Ewerth

Released as a article .

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.
In text/plain format

Archived Files and Locations

application/pdf  670.6 kB
file_37i5r7d7cnblpezqdfcus7bqdi
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2019-04-03
Version   v2
Language   en ?
arXiv  1806.07309v2
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 895f9016-0834-4d95-9099-aef716f9fb7e
API URL: JSON