1 Modeling and Analyzing the Topicality of Art Images Xiaoli Huang Business School, Sun Yat-sen University, No. 135 Xingang West Road, Guangzhou 510275, China. Phone: +86 18666080131; email: hxiaoli3@sysu.edu.cn Dagobert Soergel Graduate School of Education, the State University of New York, 536 Baldy Hall, Buffalo, NY 14260. Phone: +1 716-645-1474; email: dsoergel@buffalo.edu Judith L. Klavans University of Maryland Institute for Advanced Computer Studies (UMIACS), University of Maryland, College Park, MD 20742. Phone: +1 301-405-2033; email: jklavans@umd.edu Abstract: The goal of this paper is to present a model for an enriched understanding of art image topicality with a focus on art images through conceptual modeling and data manifestation. We present a conceptual framework for analyzing image topicality, explicating the layers, the perspectives, and the topical relevance relationships involved in modeling the topicality of images. We adapt a generic relevance typology to image analysis by extending it with definitions and relationships specific to the visual art domain and integrating it with schemes of image-text relationships that are important for image subject indexing. We apply the typology to analyze the topical relevance relationships between 11 art images and 768 image tags assigned by 18 art historians/librarians to these images. The results of our study demonstrate an improved and novel conceptual foundation to support well-structured analysis of image topicality. Specifying relevance relationships between the image and the assigned tags organizes the tags into a faceted topical structure that helps to understand and disambiguate the tags. The original contribution of our work is the topical structure analysis which allows the viewer to more easily grasp the content, context, and meaning of an image and quickly tune into aspects of interest; it could also guide both the indexer and the searcher to specify image tags/descriptors in a more systematic and precise manner and thus improve the match between the two parties. An additional contribution is systematically examining and integrating the variety of image-text relationships from a relevance perspective. The paper concludes with implications for relational indexing and social tagging. Huang, X., Soergel, D., & Klavans, J. L. (2015). Modeling and analyzing the topicality of art images. Journal of the Association for Information Science and Technology, 66(8), 1616-1644.