Search (28 results, page 1 of 2)

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Date

22. 1.2012 13:02:10

Source

Knowledge organization. 39(2012) no.1, S.13-22

Type

a

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Abstract

This paper reports on the preliminary findings of a study that explores mental associations made by novices viewing art images. In a controlled environment, 20 Taiwanese college students responded to the question "What does the painting remind you of?" after viewing each digitized image of 15 oil paintings by a famous Taiwanese artist. Rather than focusing on the representation or interpretation of art, the study attempted to solicit information about how non-experts are stimulated by art. This paper reports on the analysis of participant responses to three of the images, and describes a12-type taxonomy of association emerged from the analysis. While 9 of the types are derived and adapted from facets in the Art & Architecture Thesaurus, three new types - Artistic Influence Association, Reactive Association, and Prototype Association - are discovered. The conclusion briefly discusses both the significance of the findings and the implications for future research.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

Type

a

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Abstract

Purpose - The paper aims to explain the character and causes of obsolescence in assigned subject descriptors. Design/methodology/approach - The paper takes the form of a conceptual analysis with examples and reference to existing literature. Findings - Subject description comes in two forms: assigning the name or code of a subject to a document and assigning a document to a named subject category. Each method associates a document with the name of a subject. This naming activity is the site of tensions between the procedural need of information systems for stable records and the inherent multiplicity and instability of linguistic expressions. As languages change, previously assigned subject descriptions become obsolescent. The issues, tensions, and compromises involved are introduced. Originality/value - Drawing on the work of Robert Fairthorne and others, an explanation of the unavoidable obsolescence of assigned subject headings is presented. The discussion relates to libraries, but the same issues arise in any context in which subject description is expected to remain useful for an extended period of time.

Type

a

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Abstract

While it is easy to identify whether someone has found a piece of information during a search task, it is much harder to measure how much someone has learned during the search process. Searchers who are learning often exhibit exploratory behaviors, and so current research is often focused on improving support for exploratory search. Consequently, we need effective measures of learning to demonstrate better support for exploratory search. Some approaches, such as quizzes, measure recall when learning from a fixed source of information. This research, however, focuses on techniques for measuring open-ended learning, which often involve analyzing handwritten summaries produced by participants after a task. There are two common techniques for analyzing such summaries: (a) counting facts and statements and (b) judging topic coverage. Both of these techniques, however, can be easily confounded by simple variables such as summary length. This article presents a new technique that measures depth of learning within written summaries based on Bloom's taxonomy (B.S. Bloom & M.D. Engelhart, 1956). This technique was generated using grounded theory and is designed to be less susceptible to such confounding variables. Together, these three categories of measure were compared by applying them to a large collection of written summaries produced in a task-based study, and our results provide insights into each of their strengths and weaknesses. Both fact-to-statement ratio and our own measure of depth of learning were effective while being less affected by confounding variables. Recommendations and clear areas of future work are provided to help continued research into supporting sensemaking and learning.

Type

a

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Abstract

In this paper, we focus on applying sentiment analysis to resources from online art collections, by exploiting, as information source, tags intended as textual traces that visitors leave to comment artworks on social platforms. We present a framework where methods and tools from a set of disciplines, ranging from Semantic and Social Web to Natural Language Processing, provide us the building blocks for creating a semantic social space to organize artworks according to an ontology of emotions. The ontology is inspired by the Plutchik's circumplex model, a well-founded psychological model of human emotions. Users can be involved in the creation of the emotional space, through a graphical interactive interface. The development of such semantic space enables new ways of accessing and exploring art collections. The affective categorization model and the emotion detection output are encoded into W3C ontology languages. This gives us the twofold advantage to enable tractable reasoning on detected emotions and related artworks, and to foster the interoperability and integration of tools developed in the Semantic Web and Linked Data community. The proposal has been evaluated against a real-word case study, a dataset of tagged multimedia artworks from the ArsMeteo Italian online collection, and validated through a user study.

Type

a

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Abstract

This article describes the difference between cataloging images and cataloging books, the obstacles to including subject data in image cataloging records and how these obstacles can be overcome to make image collections more accessible. I call for participants to help create a subject authority reference resource for non-Western art. This article is an expanded and revised version of a presentation for the 2016 Joint ARLIS/VRA conference in Seattle.

Type

a

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Abstract

This article argues that authorial stance should be taken into account in the indexing of scientific documents. Authorial stance has been widely studied in linguistics and is a typical feature of scientific writing that reveals the uniqueness of each author's perspective, their scientific contribution, and their thinking. We argue that authorial stance guides the reading of scientific documents and that it can be used to characterize the knowledge contained in such documents. Our research has previously shown that people reading dissertations are interested both in a topic and in a document's authorial stance. Now, we would like to propose a two-tiered indexing system. Dissertations would first be divided into paragraphs; then, each information unit would be defined by topic and by the markers of authorial stance present in the document.

Type

a

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Abstract

It is widely accepted that subject indexing of an image is based on a two-dimensional approach. The first is the ofness and the second focuses on aboutness of the image. Assigning a suitable set of subject tags based on these two groups depends, to a great deal, on users' perception of the image. This study aims at analyzing users' perception of aboutness and ofness of images. 25 in-depth semi-structured interviews were conducted in two phases. In the first phase a collection of 10 widely known photographs were given to the interviewees and they were asked to assign subject tags (as many as they wanted) to each image. In the second phase some facts regarding each image were given to him / her to assign further tags (again as many as they wanted) or even modify their previous tags. The results show that the interviewees do focus both on ofness and aboutness in subject tagging; but it seems that they emphasize more on aboutness in describing images. On the other hand, as soon as the interviewees were able to distinguish the iconographical ofness, they could speak of iconographical and iconological aboutness. The results also show that subject indexers must focus on the iconographical level, especially regarding those tags which represent the ofness at this level.

Source

Categories, contexts and relations in knowledge organization: Proceedings of the Twelfth International ISKO Conference 6-9 August 2012, Mysore, India. Eds.: Neelameghan, A. u. K.S. Raghavan

Type

a

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Abstract

Aboutness ranks amongst our field's greatest bugbears. What is a work about? How can this be known? This mirrors debates within the philosophy of language, where the concept of representation has similarly evaded satisfactory definition. This paper proposes that we abandon the strong sense of the word aboutness, which seems to promise some inherent relationship between work and subject, or, in philosophical terms, between word and world. Instead, we seek an etymological reset to the older sense of aboutness as "in the vicinity, nearby; in some place or various places nearby; all over a surface." To distinguish this sense in the context of information studies, we introduce the term episemantics. The authors have each independently applied this term in slightly different contexts and scales (Hauser 2018a; Tennis 2016), and this article presents a unified definition of the term and guidelines for applying it at the scale of both words and works. The resulting weak concept of aboutness is pragmatic, in Star's sense of a focus on consequences over antecedents, while reserving space for the critique and improvement of aboutness determinations within various contexts and research programs. The paper finishes with a discussion of the implication of the concept of episemantics and methodological possibilities it offers for knowledge organization research and practice. We draw inspiration from Melvil Dewey's use of physical aroundness in his first classification system and ask how aroundness might be more effectively operationalized in digital environments.

Type

a

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Abstract

The philosophy of history can help articulate problems relevant to information organization. One such problem is "aboutness": How do texts relate to the world? In response to this problem, philosophers of history have developed theories of colligation describing how authors bind together phenomena under organizing concepts. Drawing on these ideas, I present a theory of subject analysis that avoids the problematic illusion of an independent "landscape" of subjects. This theory points to a broad vision of the future of information organization and some specific challenges to be met.

Type

a

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Abstract

The article is an introduction to the development of Andersen's concept of textual tools used in knowledge organization (KO) in light of the theory of genres and activity systems. In particular, the question is based on the concepts of genre connectivity and genre group, in addition to previously established concepts such as genre hierarchy, set, system, and repertoire. Five genre groups used in KO are described. The analysis of groups, systems, and selected genres used in KO is provided, based on the method proposed by Yates and Orlikowski. The aim is to show the genre system as a part of the activity system, and thus as a framework for KO.

Type

a

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Source

Categories, contexts and relations in knowledge organization: Proceedings of the Twelfth International ISKO Conference 6-9 August 2012, Mysore, India. Eds.: Neelameghan, A. u. K.S. Raghavan

Type

a

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Abstract

Emotional meaning is critical for users to retrieve relevant images. However, because emotional meanings are subject to the individual viewer's interpretation, they are considered difficult to implement when designing image retrieval systems. With the intent of making an image's emotional messages more readily accessible, this study aims to test a new approach designed to enhance the accessibility of emotional meanings during the image search process. This approach utilizes image searchers' emotional reactions, which are quantitatively measured. Broadly used quantitative measurements for emotional reactions, Semantic Differential (SD) and Self-Assessment Manikin (SAM), were selected as tools for gathering users' reactions. Emotional representations obtained from these two tools were compared with three image perception tasks: searching, describing, and sorting. A survey questionnaire with a set of 12 images was administered to 58 participants, which were tagged with basic emotions. Results demonstrated that the SAM represents basic emotions on 2-dimensional plots (pleasure and arousal dimensions), and this representation consistently corresponded to the three image perception tasks. This study provided experimental evidence that quantitative users' reactions can be a useful complementary element of current image retrieval/indexing systems. Integrating users' reactions obtained from the SAM into image browsing systems would reduce the efforts of human indexers as well as improve the effectiveness of image retrieval systems.

Type

a

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Abstract

This study demonstrates an improved conceptual foundation to support well-structured analysis of image topicality. First 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 art 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 then apply the adapted typology to analyze the topical relevance relationships between 11 art images and 768 image tags assigned by art historians and librarians. The original contribution of our work is the topical structure analysis of image tags that 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.

Type

a

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Abstract

Creative professionals in advertising, marketing, design and journalism search for images to visually represent a concept for their project. The main purpose of this paper is to present search facets derived from an analysis of documents known as briefs, which are widely used in creative industries as requirement documents describing information needs. The briefs specify the type of image required, such as the content and context of use for the image and represent the topic from which the searcher builds an image query. We take three main sources-user image search behaviour, briefs, and image search engine search facets-to examine the search facets for image searching in order to examine the following research question-are search facet schemes for image search engines sufficient for user needs, or is revision needed? We found that there are three main classes of user search facet, which include business, contextual and image related information. The key argument in the paper is that the facet "keyword/tag" is ambiguous and does not support user needs for more generic descriptions to broaden search or specific descriptions to narrow their search-we suggest that a more detailed search facet scheme would be appropriate.

Type

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Abstract

This article presents and discusses the concept "subject" or subject matter (of documents) as it has been examined in library and information science (LIS) for more than 100 years. Different theoretical positions are outlined and it is found that the most important distinction is between documentoriented views versus request-oriented views. The documentoriented view conceives subject as something inherent in documents, whereas the request-oriented view (or the policybased view) understands subject as an attribution made to documents in order to facilitate certain uses of them. Related concepts such as concepts, aboutness, topic, isness and ofness are also briefly presented. The conclusion is that the most fruitful way of defining "subject" (of a document) is the document's informative or epistemological potentials, that is, the document's potentials of informing users and advancing the development of knowledge.

Type

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Abstract

This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.

Type

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Abstract

Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine-learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.

Type

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Abstract

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

Type

a