Rekabsaz, Navid and Bierig, Ralf and Lupu, Mihai and Hanbury, Allan (2017) Toward Optimized Multimodal Concept Indexing. Transactions on Computational Collective Intelligence XXVI. pp. 144-161.

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Abstract

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

Item Type:	Article
Keywords:	Semantic indexing; Concept; Social web; Word2Vec;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15209
Identification Number:	https://doi.org/10.1007/978-3-319-59268-8_7
Depositing User:	Ralf Bierig
Date Deposited:	10 Jan 2022 16:41
Journal or Publication Title:	Transactions on Computational Collective Intelligence XXVI
Publisher:	Springer
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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