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Zeitschriftenartikel:

N. Rekabsaz, R. Bierig, M. Lupu, A. Hanbury:
"Toward Optimized Multimodal Concept Indexing";
Journal of Transactions on Computational Collective Intelligence (TCCI), 10190 (2017), XXVI; S. 144 - 161.



Kurzfassung englisch:
Information retrieval on the (social) web moves from a pure term-frequency-based approach to an enhanced method that includes conceptual multimodal features on a semantic level. In this paper, we present an approach for semantic-based keyword search and focus especially on its optimization to scale it to real-world sized collections in the social media domain. Furthermore, we present a faceted indexing framework and architecture that relates content to semantic concepts to be indexed and searched semantically. We study the use of textual concepts in a social media domain and observe a significant improvement from using a concept-based solution for keyword searching. We address the problem of time-complexity that is a critical issue for concept-based methods by focusing on optimization to enable larger and more real-world style applications.

Schlagworte:
Semantic indexing, Concept, Social web, Word2Vec


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1007/978-3-319-59268-8


Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.