Literatur zur Informationserschließung

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)

Suchergebnisse

• 1
  Sun, X. ; Zhou, X. ; Wang, Q. ; Sharples, S.: Investigating the impact of emotions on perceiving serendipitous information encountering.
  In: Journal of the Association for Information Science and Technology. 73(2022) no.1, S.3-18.
  
  Abstract: Despite the potential importance of emotional aspects in information seeking, there is a lack of adequate attention to emotions' role in facilitating serendipitous information encountering. This paper contributes to this research gap by investigating the role of emotions during the process of perceiving and experiencing serendipitous information encountering in a controlled laboratory setting. The results show that applying a sketch game can stimulate participants' emotions. Our findings indicate that participants are more likely to experience serendipitous information encountering under the influence of positive emotions. This study contributes to an understanding of the relationship between emotions and the perception of serendipitous information encountering. The implications of the possibilities of facilitating positive emotions to induce serendipitous information encountering are discussed.
  
  Inhalt: Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24540.
  
  Themenfeld: Suchtaktik
  
  
• 2
  Zhou, X. ; Sun, X. ; Wang, Q. ; Sharples, S.: ¬A context-based study of serendipity in information research among Chinese scholars.
  In: Journal of documentation. 74(2018) no.3, S.526-551.
  
  Abstract: Purpose The current understanding of serendipity is based primarily on studies employing westerners as the participants, and it remains uncertain whether or not this understanding would be pervasive under different cultures, such as in China. In addition, there is not a sufficient systematic investigation of context during the occurrence of serendipity in current studies. The purpose of this paper is to examine the above issues by conducting a follow-up empirical study with a group of Chinese scholars. Design/methodology/approach The social media application "WeChat" was employed as a research tool. A diary-based study was conducted and 16 participants were required to send to the researchers any cases of serendipity they encountered during a period of two weeks, and this was followed by a post-interview. Findings Chinese scholars experienced serendipity in line with the three main processes of: encountering unexpectedness, connection-making and recognising the value. An updated context-based serendipity model was constructed, where the role of context during each episode of experiencing serendipity was identified, including the external context (e.g. time, location and status), the social context and the internal context (e.g. precipitating conditions, sagacity/perceptiveness and emotion). Originality/value The updated context model provides a further understanding of the role played by context during the different processes of serendipity. The framework for experiencing serendipity has been expanded, and this may be used to classify the categories of serendipity.
  
  Inhalt: Vgl.: https://www.emeraldinsight.com/doi/full/10.1108/JD-05-2017-0079.
  
  Themenfeld: Informationsdienstleistungen ; Benutzerstudien
  
  
• 3
  Jiang, X. ; Sun, X. ; Yang, Z. ; Zhuge, H. ; Lapshinova-Koltunski, E. ; Yao, J.: Exploiting heterogeneous scientific literature networks to combat ranking bias : evidence from the computational linguistics area.
  In: Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1679-1702.
  
  Abstract: It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.
  
  Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23463/abstract.
  
  Themenfeld: Retrievalalgorithmen
  
  
• 4
  Sun, X. ; Lin, H.: Topical community detection from mining user tagging behavior and interest.
  In: Journal of the American Society for Information Science and Technology. 64(2013) no.2, S.321-333.
  
  Abstract: With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.
  
  Themenfeld: Data Mining
  
  
• 5
  Lin, Y. ; Lin, H. ; Xu, K. ; Sun, X.: Learning to rank using smoothing methods for language modeling.
  In: Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.818-828.
  
  Abstract: The central issue in language model estimation is smoothing, which is a technique for avoiding zero probability estimation problem and overcoming data sparsity. There are three representative smoothing methods: Jelinek-Mercer (JM) method; Bayesian smoothing using Dirichlet priors (Dir) method; and absolute discounting (Dis) method, whose parameters are usually estimated empirically. Previous research in information retrieval (IR) on smoothing parameter estimation tends to select a single value from optional values for the collection, but it may not be appropriate for all the queries. The effectiveness of all the optional values should be considered to improve the ranking performance. Recently, learning to rank has become an effective approach to optimize the ranking accuracy by merging the existing retrieval methods. In this article, the smoothing methods for language modeling in information retrieval (LMIR) with different parameters are treated as different retrieval methods, then a learning to rank approach to learn a ranking model based on the features extracted by smoothing methods is presented. In the process of learning, the effectiveness of all the optional smoothing parameters is taken into account for all queries. The experimental results on the Learning to Rank for Information Retrieval (LETOR) LETOR3.0 and LETOR4.0 data sets show that our approach is effective in improving the performance of LMIR.