Rekabsaz, N., Bierig, R., Lupu, M., & Hanbury, A. (2015). Toward Optimized Multimodal Concept Indexing. In J. Cardoso, F. Guerra, G.-J. Houben, A. M. Pinto, & Y. Velegrakis (Eds.), Semantic Keyword-based Search on Structured Data Sources: First COST Action IC1302 International KEYSTONE Conference, IKC 2015, Coimbra, Portugal, September 8-9, 2015. Revised Selected Papers (pp. 141–152). Springer. https://doi.org/10.1007/978-3-319-27932-9_13
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 critical issue for concept-based methods by focusing on optimization to enable larger and more real-world style applications.
Computer Engineering and Software-Intensive Systems: 100%