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  • × author_ss:"MacFarlane, A."
  • × year_i:[2020 TO 2030}
  1. Berget, G.; MacFarlane, A.: What Is known about the impact of impairments on information seeking and searching? (2020) 0.00 
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    Abstract
    Information seeking and access are essential for users in all walks of life, from addressing personal needs such as finding flights to locating information needed to complete work tasks. Over the past decade or so, the general needs of people with impairments have increasingly been recognized as something to be addressed, an issue embedded both in international treaties and in state legislation. The same tendency can be found in research, where a growing number of user studies including people with impairments have been conducted. The purpose of these studies is typically to uncover potential barriers for access to information, especially in the context of inaccessible search user interfaces. This literature review provides an overview of research on the information seeking and searching of users with impairments. The aim is to provide an overview to both researchers and practitioners who work with any of the user groups identified. Some diagnoses are relatively well represented in the literature (for instance, visual impairment), but there is very little work in other areas (for instance, autism) and in some cases no work at all (for instance, aphasia). Gaps are identified in the research, and suggestions are made regarding areas where further research is needed.
  2. MacFarlane, A.; Missaoui, S.; Frankowska-Takhari, S.: On machine learning and knowledge organization in multimedia information retrieval (2020) 0.00 
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    Abstract
    Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field, and solutions to them are many years off. However, new technological developments allow the use of knowledge organization and machine learning in multimedia search systems and services. Specifically, we argue that, the improvement of detection of some classes of lowlevel features in images music and video can be used in conjunction with knowledge organization to tag or label multimedia content for better retrieval performance. We provide an overview of the use of knowledge organization schemes in machine learning and make recommendations to information professionals on the use of this technology with knowledge organization techniques to solve multimedia IR problems. We introduce a five-step process model that extracts features from multimedia objects (Step 1) from both knowledge organization (Step 1a) and machine learning (Step 1b), merging them together (Step 2) to create an index of those multimedia objects (Step 3). We also overview further steps in creating an application to utilize the multimedia objects (Step 4) and maintaining and updating the database of features on those objects (Step 5).
    Content
    DOI:10.5771/0943-7444-2020-1-45.
    Source
    Knowledge organization. 47(2020) no.1, S.45-55

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