Rekabsaz, N., Bierig, R., Lupu, M., & Hanbury, A. (2017). Toward Optimized Multimodal Concept Indexing. In A. M. Pinto & J. Cardoso (Eds.), Transactions on Computational Collective Intelligence XXVI (Vol. 10190, pp. 144–161). Springer. https://doi.org/10.1007/978-3-319-59268-8_7
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.
