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On machine learning and knowledge organisation in Multimedia Information Retrieval

MacFarlane, A., Missaoui, S. & Frankowska-Takhari, S. (2020). On machine learning and knowledge organisation in Multimedia Information Retrieval. Proceedings of ISKO UK Sixth Biennial Conference: The Human Position in an Artificial World: Creativity, Ethics and AI in Knowledge Organization., 47(1), pp. 45-55. doi: 10.5771/0943-7444-2020-1-45


Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval (IR). Deployment of machine-learning techniques is widespread in text search, notability web search engines (Dai et al., 2011). Multimedia information retrieval as a problem however still represents a significant challenge to machine learning as a technological solution, but some problems in IR can still be addressed by using appropriate AI techniques. In this paper we review the technological developments, and provide a perspective on the use of machine-learning techniques in conjunction with knowledge organisation techniques to address multimedia IR needs. We take the perspective from the MacFarlane (2016) position paper, that there are some problems in multimedia IR that AI and machine learning cannot currently solve. The semantic gap in multimedia IR (Enser, 2008) remains a significant problem in the field, and solutions to them are many years off. However, there are occasions where the new technological developments allow the use of knowledge organisation and machine learning in multimedia search systems and services. Specifically we argue that the improvement of detection of some classes of low level features in images (Karpathy and Li, 2015), music (Byrd and Crawford, 2002) and video (Hu et al., 2011) can be used in conjunction with knowledge organisation to tag or label multimedia content for better retrieval performance. We advocate the use of supervised learning techniques. We provide an overview of the use of knowledge organisation schemes in machine learning, and make recommendations to information professionals on the use of this technology with knowledge organisation techniques to solve multimedia IR problems.

Publication Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments: School of Science & Technology > Computer Science > Human Computer Interaction Design
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