Towards to an automatic semantic annotation for multimedia learning objects

Abstract

The number of digital video recordings has increased dramatically. The idea of recording lectures, speeches, and other academic events is not new. But, the accessibility and traceability of its content for further use is rather limited. Searching multimedia data, in particular audiovisual data, is still a challenging task to overcome. We describe and evaluate a new approach to generate a semantic annotation for multimedia resources, i.e., recorded university lectures. Speech recognition is applied to create a tentative and deficient transliteration of the video recordings. We show that the imperfect transliteration is sufficient to generate semantic metadata serialized in an OWL file. The semantic annotation process based on textual material and deficient transliterations of lecture recordings are discussed and evaluated.

Faceted search and retrieval based on semantically annotated product family ontology

“…The existing video annotation approaches are based on the analysis of transliteration or transcripts of a video recording [21][22][23], or from the motion detected and extracted from a video recording [24,25]. The later approach is usually ontology-based, where the ontology serves as the knowledge foundation for annotation.…”

“…The later approach is usually ontology-based, where the ontology serves as the knowledge foundation for annotation. Currently, efforts reported in video annotation focus on ontology-assisted manual annotation [26], clip pattern matching and annotation [24,25] and transcript based language processing [22,23].…”

Question answering from lecture videos based on an automatic semantic annotation

“…deleting stop-words and stemming of the words -the stems are stored in a database. This part of our system has already been described in [13,14].…”

Automatic Extraction of Semantic Descriptions from the Lecturer's Speech

“…The reliability of our solution is evaluated via different benchmark tests. This paper is based on the research of [16,17,18]. In addition, we present two methods for the automatic generation of semantic descriptions as well as the comparison of our results with a manually-generated transcript corpus (an error free transcript), the MRR evaluation dimension and the consideration of the chronological order of the learning objects in the lecture videos.…”