On Machine Learning and Knowledge Organization in Multimedia Information Retrieval
| Autor: | Andrew MacFarlane, S. Frankowska-Takhari, Sondess Missaoui |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Multimedia search
business.industry Computer science Knowledge economy 05 social sciences Supervised learning Full text search Multimedia information retrieval Library and Information Sciences Machine learning computer.software_genre Field (computer science) Notability Artificial intelligence 0509 other social sciences 050904 information & library sciences business computer Semantic gap |
| Zdroj: | KNOWLEDGE ORGANIZATION. 47:45-55 |
| ISSN: | 0943-7444 |
| DOI: | 10.5771/0943-7444-2020-1-45 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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