Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 28. April 2022)
1Zhang, J. ; Chen, Y. ; Zhao, Y. ; Wolfram, D. ; Ma, F.: Public health and social media : a study of Zika virus-related posts on Yahoo! Answers.
In: Journal of the Association for Information Science and Technology. 71(2020) no.3, S.282-299.
Abstract: This study investigates the content of questions and responses about the Zika virus on Yahoo! Answers as a recent example of how public concerns regarding an international health issue are reflected in social media. We investigate the contents of posts about the Zika virus on Yahoo! Answers, identify and reveal subject patterns about the Zika virus, and analyze the temporal changes of the revealed subject topics over 4 defined periods of the Zika virus outbreak. Multidimensional scaling analysis, temporal analysis, and inferential statistical analysis approaches were used in the study. A resulting 2-layer Zika virus schema, and term connections and relationships are presented. The results indicate that consumers' concerns changed over the 4 defined periods. Consumers paid more attention to the basic information about the Zika virus, and the prevention and protection from the Zika virus at the beginning of the outbreak of the Zika virus. During the later periods, consumers became more interested in the role that the government and health organizations played in the public health emergency.
Inhalt: Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24245.
2Zhao, Y. ; Ma, F. ; Xia, X.: Evaluating the coverage of entities in knowledge graphs behind general web search engines : Poster.
In: http://www.iskocus.org/NASKO2017papers/NASKO2017_paper_10.pdf [NASKO 2017, June 15-16, 2017, Champaign, IL, USA].
Abstract: Web search engines, such as Google and Bing, are constantly employing results from knowledge organization and various visualization features to improve their search services. Knowledge graph, a large repository of structured knowledge represented by formal languages such as RDF (Resource Description Framework), is used to support entity search feature of Google and Bing (Demartini, 2016). When a user searchs for an entity, such as a person, an organization, or a place in Google or Bing, it is likely that a knowledge cardwill be presented on the right side bar of the search engine result pages (SERPs). For example, when a user searches the entity Benedict Cumberbatch on Google, the knowledge card will show the basic structured information about this person, including his date of birth, height, spouse, parents, and his movies, etc. The knowledge card, which is used to present the result of entity search, is generated from knowledge graphs. Therefore, the quality of knowledge graphs is essential to the performance of entity search. However, studies on the quality of knowledge graphs from the angle of entity coverage are scant in the literature. This study aims to investigate the coverage of entities of knowledge graphs behind Google and Bing.
Inhalt: Beitrag bei: NASKO 2017: Visualizing Knowledge Organization: Bringing Focus to Abstract Realities. The sixth North American Symposium on Knowledge Organization (NASKO 2017), June 15-16, 2017, in Champaign, IL, USA.
Objekt: Google ; Bing
3Xu, C. ; Ma, B. ; Chen, X. ; Ma, F.: Social tagging in the scholarly world.
In: Journal of the American Society for Information Science and Technology. 64(2013) no.10, S.2045-2057.
Abstract: The number of research studies on social tagging has increased rapidly in the past years, but few of them highlight the characteristics and research trends in social tagging. A set of 862 academic documents relating to social tagging and published from 2005 to 2011 was thus examined using bibliometric analysis as well as the social network analysis technique. The results show that social tagging, as a research area, develops rapidly and attracts an increasing number of new entrants. There are no key authors, publication sources, or research groups that dominate the research domain of social tagging. Research on social tagging appears to focus mainly on the following three aspects: (a) components and functions of social tagging (e.g., tags, tagging objects, and tagging network), (b) taggers' behaviors and interface design, and (c) tags' organization and usage in social tagging. The trend suggest that more researchers turn to the latter two integrated with human computer interface and information retrieval, although the first aspect is the fundamental one in social tagging. Also, more studies relating to social tagging pay attention to multimedia tagging objects and not only text tagging. Previous research on social tagging was limited to a few subject domains such as information science and computer science. As an interdisciplinary research area, social tagging is anticipated to attract more researchers from different disciplines. More practical applications, especially in high-tech companies, is an encouraging research trend in social tagging.
Themenfeld: Social tagging ; Informetrie
4Veenema, F.: To index or not to index.
In: Canadian journal of information and library science. 21(1996) no.2, S.1-22.
Abstract: Describes an experiment comparing the performance of automatic full-text indexing software for personal computers with the human intellectual assignment of indexing terms in each document in a collection. Considers the times required to index the document, to retrieve documents satisfying 5 typical foreseen information needs, and the recall and precision ratios of searching. The software used is QuickFinder facility in WordPerfect 6.1 for Windows