Alkim Almila Akdag Salah

The structure of the Arts & Humanities Citation Index : A mapping on the basis of aggregated citations among 1,157 journals

Using the Arts & Humanities Citation Index (A&HCI) 2008, we apply mapping techniques previously developed for mapping journal structures in the Science and Social Sciences Citation Indices. Citation relations among the 110,718 records were aggregated at the level of 1,157 journals specific to the A&HCI, and the journal structures are questioned on whether a cognitive structure can be reconstructed and visualized. Both cosine-normalization (bottom up) and factor analysis (top down) suggest a division into approximately 12 subsets. The relations among these subsets are explored using various visualization techniques. However, we were not able to retrieve this structure using the Institute for Scientific Information Subject Categories, including the 25 categories that are specific to the A&HCI. We discuss options for validation such as against the categories of the Humanities Indicators of the American Academy of Arts and Sciences, the panel structure of the European Reference Index for the Humanities, and compare our results with the curriculum organization of the Humanities Section of the College of Letters and Sciences of the University of California at Los Angeles as an example of institutional organization.

Need to Categorize: A Comparative Look at the Categories of Universal Decimal Classification System and Wikipedia

This study analyzes the differences between the category structure of the Universal Decimal Classification (UDC) system (which is one of the widely used library classification systems in Europe) and Wikipedia. In particular, the authors compare the emerging structure of category-links to the structure of classes in the UDC. The authors scrutinize the question of how knowledge maps of the same domain differ when they are created socially (i.e. Wikipedia) as opposed to when they are created formally (UDC) using classification theory. As a case study, we focus on the category of “Arts”.

EVOLUTION OF WIKIPEDIA'S CATEGORY STRUCTURE

Wikipedia, as a social phenomenon of collaborative knowledge creation, has been studied extensively from various points of view. The category system of Wikipedia, introduced in 2004, has attracted relatively little attention. In this study, we focus on the documentation of knowledge, and the transformation of this documentation with time. We take Wikipedia as a sample of knowledge in general and its category system as an aspect of the structure of this knowledge. We investigate the evolution of the category structure of the English Wikipedia from its birth in 2004 to 2008. We treat the category system as if it is a hierarchical Knowledge Organization System, capturing the changes in the distributions of the top categories. We investigate how the clustering of articles, defined by the category system, matches the direct link network between the articles and show how it changes over time. We find the Wikipedia category network mostly stable, but with occasional reorganization. We show that the clustering matches the link structure quite well, except short periods preceding the reorganizations.

Technoscience Art : A Bridge Between Neuroesthetics and Art History?

One of the most exciting recent developments in mainstream art history is its confrontation with the cognitive sciences and neurology. This study is based on the problems these disciplines face before they can contribute to each other. The authors inspect several critical issues resulting from this encounter, especially in the new field of neuroesthetics. The authors argue that it is a language barrier between the disciplines, rather than any fundamental conceptual division, that causes the lack of understanding on both sides. Shared terms in arts and neuroscience are elusive, and the different connotations of extant terms in these separate disciplines must be addressed. The authors propose technoscience art as a ground in which joint terminology may be developed, an audience familiar to the concerns of both sides can be formed, and a new generation of scientifically knowledgeable artists and scientists can interact for their mutual benefit.

Significance of visual interfaces in institutional and user-generated databases with category structures

In this paper we visually explore the data structure of two different visual platforms: the database behind the social environment of a social networking site, and the intricate infrastructure of a research institute for preservation of deposited datasets. We argue that visual analytics of metadata of collections can be used in multiple ways: for the backend users, to inform the archive about structure and growth of its collection; to foster collection strategies; and to check metadata consistency, for the end-users, to give an overview to the collections, and thus to generate more awareness of the collection and its metadata, to give the enduser extra information to contextualize the entirety of the archive. We conclude with a discussion on how text based search combined with different type of visually enhanced browsing improves data access, navigation, and reuse in these two radically different contexts.

A New Database and Protocol for Image Reuse Detection

The use of visual elements of an existing image while creating new ones is a commonly observed phenomenon in digital artworks. The practice, which is referred to as image reuse, is not an easy one to detect even with the human eye, less so using computational methods. In this paper, we study the automatic image reuse detection in digital artworks as an image retrieval problem. First, we introduce a new digital art database (BODAIR) that consists of a set of digital artworks that re-use stock images. Then, we evaluate a set of existing image descriptors for image reuse detection, providing a baseline for the detection of image reuse in digital artworks. Finally, we propose an image retrieval method tailored for reuse detection, by combining saliency maps with the image descriptors.

Knowledge Maps of the UDC: Uses and Use Cases

Insight into the depth and breadth of knowledge for use in and across disciplines is of vital importance. Our knowledge maps are visualizations based on empirical evidence about both collection characteristics and knowledge clusters such as disciplines. We report in this paper on collaborative efforts over several years, combining the resources of the Knowledge Space Lab and the Research and Innovation Group at DANS. In particular, we were interested in the narrative of how knowledge and knowledge systems change over time. Knowledge organization systems are evolving complex systems. Their analysis, both concerning inner structure, evolution over time, and their implementation in information spaces is important to better understand how knowledge is produced and can be navigated through.

Emotions in Abstract Art: Does Texture Matter?

The classification of images based on the emotions they evoke is a recent approach in multimedia. With the abundance of digitized images from museum archives and the ever-growing digital production of user-generated images, there is a greater need for intelligent image retrieval algorithms. Categorization of images according to their emotional impact offers a useful addition to the state of the art in image search. In this work, we apply computer vision techniques on abstract paintings to automatically predict emotional valence based on texture. We also propose a method to derive a small set of features (Perlin parameters) from an image to represent its overall texture. Finally, we investigate the saliency distribution in these images, and show that computational models of bottom-up attention can be used to predict emotional valence in a parsimonious manner.

The Development of the Journal Environment of Leonardo

The authors present animations based on the aggregated journal-journal citations of Leonardo during the period 1974–2008. Leonardo is mainly cited by journals outside the arts domain for cultural reasons, for example, in neuropsychology and physics. Articles in Leonardo itself cite a large number of journals, but with a focus on the arts. Animations at this level of aggregation enable us to show the history of the journal from a network perspective.