Abstract
We present a novel approach, called SemI, to semantic indexing of annotated multimedia objects for their efficient retrieval. The generation of multimedia indices with SemI relies on the semantic annotation of these objects with references to concepts formally defined in standard OWL2 and semantic services described in OWL-S. For scoring the annotated multimedia data in these indices an appropriate semantic similarity measure makes use of approximated logical concept abduction in order to alleviate strict logical false negatives. Efficient query answering over SemI indices is performed with the use of Fagin’s threshold algorithm. The results of our comparative experimental evaluation reveals that SemI-enabled multimedia retrieval can significantly outperform representative approaches of LSA- and RDF-based semantic retrieval in this domain in terms of precision at recall, averaged precision and discounted cumulative gain.
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Cao, X., Klusch, M. (2014). Semantic Indexing for Efficient Retrieval of Multimedia Data. In: Nürnberger, A., Stober, S., Larsen, B., Detyniecki, M. (eds) Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation. AMR 2012. Lecture Notes in Computer Science(), vol 8382. Springer, Cham. https://doi.org/10.1007/978-3-319-12093-5_9
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