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: 04. Juni 2021)
1Wang, F. ; Wang, X.: Tracing theory diffusion : a text mining and citation-based analysis of TAM.
In: Journal of documentation. 76(2020) no.6, S.1109-1134.
Abstract: Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM). Design/methodology/approach Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution. Findings It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration. Research limitations/implications The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation. Originality/value The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies.
Inhalt: Vgl.: https://doi.org/10.1108/JD-02-2020-0023.
2Shen, X.-L. ; Li, Y.-J. ; Sun, Y. ; Chen, J. ; Wang, F.: Knowledge withholding in online knowledge spaces : social deviance behavior and secondary control perspective.
In: Journal of the Association for Information Science and Technology. 70(2019) no.4, S.385-401.
Abstract: Knowledge withholding, which is defined as the likelihood that an individual devotes less than full effort to knowledge contribution, can be regarded as an emerging social deviance behavior for knowledge practice in online knowledge spaces. However, prior studies placed a great emphasis on proactive knowledge behaviors, such as knowledge sharing and contribution, but failed to consider the uniqueness of knowledge withholding. To capture the social-deviant nature of knowledge withholding and to better understand how people deal with counterproductive knowledge behaviors, this study develops a research model based on the secondary control perspective. Empirical analyses were conducted using the data collected from an online knowledge space. The results indicate that both predictive control and vicarious control exert a positive influence on knowledge withholding. This study also incorporates knowledge-withholding acceptability as a moderating variable of secondary control strategies. In particular, knowledge-withholding acceptability enhances the impact of predictive control, whereas it weakens the effect of vicarious control on knowledge withholding. This study concludes with a discussion of the key findings, and the implications for both research and practice.
Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24192.
Anmerkung: Beitrag eines Special issue on social informatics of knowledge
3Lee, J. ; Oh, S. ; Dong, H. ; Wang, F. ; Burnett, G.: Motivations for self-archiving on an academic social networking site : a study on researchgate.
In: Journal of the Association for Information Science and Technology. 70(2019) no.6, S.563-574.
Abstract: This study investigates motivations for self-archiving research items on academic social networking sites (ASNSs). A model of these motivations was developed based on two existing motivation models: motivation for self-archiving in academia and motivations for information sharing in social media. The proposed model is composed of 18 factors drawn from personal, social, professional, and external contexts, including enjoyment, personal/professional gain, reputation, learning, self-efficacy, altruism, reciprocity, trust, community interest, social engagement, publicity, accessibility, self-archiving culture, influence of external actors, credibility, system stability, copyright concerns, additional time, and effort. Two hundred and twenty-six ResearchGate users participated in the survey. Accessibility was the most highly rated factor, followed by altruism, reciprocity, trust, self-efficacy, reputation, publicity, and others. Personal, social, and professional factors were also highly rated, while external factors were rated relatively low. Motivations were correlated with one another, demonstrating that RG motivations for self-archiving could increase or decrease based on several factors in combination with motivations from the personal, social, professional, and external contexts. We believe the findings from this study can increase our understanding of users' motivations in sharing their research and provide useful implications for the development and improvement of ASNS services, thereby attracting more active users.
Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24138.
4Xu, S. ; Zhai, D. ; Wang, F. ; An, X. ; Pang, H. ; Sun, Y.: ¬A novel method for topic linkages between scientific publications and patents.
In: Journal of the Association for Information Science and Technology. 70(2019) no.9, S.1026-1042.
Abstract: It is increasingly important to build topic linkages between scientific publications and patents for the purpose of understanding the relationships between science and technology. Previous studies on the linkages mainly focus on the analysis of nonpatent references on the front page of patents, or the resulting citation-link networks, but with unsatisfactory performance. In the meanwhile, abundant mentioned entities in the scholarly articles and patents further complicate topic linkages. To deal with this situation, a novel statistical entity-topic model (named the CCorrLDA2 model), armed with the collapsed Gibbs sampling inference algorithm, is proposed to discover the hidden topics respectively from the academic articles and patents. In order to reduce the negative impact on topic similarity calculation, word tokens and entity mentions are grouped by the Brown clustering method. Then a topic linkages construction problem is transformed into the well-known optimal transportation problem after topic similarity is calculated on the basis of symmetrized Kullback-Leibler (KL) divergence. Extensive experimental results indicate that our approach is feasible to build topic linkages with more superior performance than the counterparts.
Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24175.
5Zhai, Y ; Ding, Y. ; Wang, F.: Measuring the diffusion of an innovation : a citation analysis.
In: Journal of the Association for Information Science and Technology. 69(2018) no.3, S.368-379.
Abstract: Innovations transform our research traditions and become the driving force to advance individual, group, and social creativity. Meanwhile, interdisciplinary research is increasingly being promoted as a route to advance the complex challenges we face as a society. In this paper, we use Latent Dirichlet Allocation (LDA) citation as a proxy context for the diffusion of an innovation. With an analysis of topic evolution, we divide the diffusion process into five stages: testing and evaluation, implementation, improvement, extending, and fading. Through a correlation analysis of topic and subject, we show the application of LDA in different subjects. We also reveal the cross-boundary diffusion between different subjects based on the analysis of the interdisciplinary studies. The results show that as LDA is transferred into different areas, the adoption of each subject is relatively adjacent to those with similar research interests. Our findings further support researchers' understanding of the impact formation of innovation.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23898/full.
6Moskovitch, R. ; Wang, F. ; Pei, J. ; Friedman, C.: JASIST special issue on biomedical information retrieval : Editorial.
In: Journal of the Association for Information Science and Technology. 68(2017) no.11, S.2525-2528.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23972/full. Vgl. das Erratum in JASIST 69(2018) no.3, S.500.
Wissenschaftsfach: Biochemie ; Medizin
7Wei, J. ; Wang, F. ; Lindell, M.K.: ¬The evolution of stakeholders' perceptions of disaster : a model of information flow.
In: Journal of the Association for Information Science and Technology. 67(2016) no.2, S.441-453.
Abstract: This paper proposes a diffusion model to measure the evolution of stakeholders' disaster perceptions by integrating a disaster message model, a stakeholder model, and a stakeholder memory model, which collectively describe the process of information flow. Simulation results show that the rate of forgetting has a significantly negative effect on stakeholders' perceptions and the incremental increase in the number of affected individuals has a positive effect on the maximum level of stakeholders' perceptions, but negative effect on the duration of stakeholders' perceptions. Additionally, a delay effect, a stagnation effect, and a cumulative effect exist in the evolution of stakeholders' perceptions. There is a spike at the beginning of the profile of stakeholders' perceptions in the Damped Exponential Model. An empirical test supports the validity of this model of stakeholders' disaster perceptions.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23386/abstract.
8Xie, H. ; Li, X. ; Wang, T. ; Lau, R.Y.K. ; Wong, T.-L. ; Chen, L. ; Wang, F.L. ; Li, Q.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy.
In: Information processing and management. 52(2016) no.1, S.61-72.
Abstract: In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.
Inhalt: Vgl.: doi:10.1016/j.ipm.2015.03.001.
Anmerkung: Beitrag in einem Themenheft "Emotion and sentiment in social and expressive media"
Themenfeld: Folksonomies ; Inhaltsanalyse
9Wang, F. ; Wolfram, D.: Assessment of journal similarity based on citing discipline analysis.
In: Journal of the Association for Information Science and Technology. 66(2015) no.6, S.1189-1198.
Abstract: This study compares the range of disciplines of citing journal articles to determine how closely related journals assigned to the same Web of Science research area are. The frequency distribution of disciplines by citing articles provides a signature for a cited journal that permits it to be compared with other journals using similarity comparison techniques. As an initial exploration, citing discipline data for 40 high-impact-factor journals assigned to the "information science and library science" category of the Web of Science were compared across 5 time periods. Similarity relationships were determined using multidimensional scaling and hierarchical cluster analysis to compare the outcomes produced by the proposed citing discipline and established cocitation methods. The maps and clustering outcomes reveal that a number of journals in allied areas of the information science and library science category may not be very closely related to each other or may not be appropriately situated in the category studied. The citing discipline similarity data resulted in similar outcomes with the cocitation data but with some notable differences. Because the citing discipline method relies on a citing perspective different from cocitations, it may provide a complementary way to compare journal similarity that is less labor intensive than cocitation analysis.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23241/abstract.
10Zhang, C. ; Zeng, D. ; Li, J. ; Wang, F.-Y. ; Zuo, W.: Sentiment analysis of Chinese documents : from sentence to document level.
In: Journal of the American Society for Information Science and Technology. 60(2009) no.12, S.2474-2487.
Abstract: User-generated content on the Web has become an extremely valuable source for mining and analyzing user opinions on any topic. Recent years have seen an increasing body of work investigating methods to recognize favorable and unfavorable sentiments toward specific subjects from online text. However, most of these efforts focus on English and there have been very few studies on sentiment analysis of Chinese content. This paper aims to address the unique challenges posed by Chinese sentiment analysis. We propose a rule-based approach including two phases: (1) determining each sentence's sentiment based on word dependency, and (2) aggregating sentences to predict the document sentiment. We report the results of an experimental study comparing our approach with three machine learning-based approaches using two sets of Chinese articles. These results illustrate the effectiveness of our proposed method and its advantages against learning-based approaches.
11Yang, C.C. ; Wang, F.L.: Hierarchical summarization of large documents.
In: Journal of the American Society for Information Science and Technology. 59(2008) no.6, S.887-902.
Abstract: Many automatic text summarization models have been developed in the last decades. Related research in information science has shown that human abstractors extract sentences for summaries based on the hierarchical structure of documents; however, the existing automatic summarization models do not take into account the human abstractor's behavior of sentence extraction and only consider the document as a sequence of sentences during the process of extraction of sentences as a summary. In general, a document exhibits a well-defined hierarchical structure that can be described as fractals - mathematical objects with a high degree of redundancy. In this article, we introduce the fractal summarization model based on the fractal theory. The important information is captured from the source document by exploring the hierarchical structure and salient features of the document. A condensed version of the document that is informatively close to the source document is produced iteratively using the contractive transformation in the fractal theory. The fractal summarization model is the first attempt to apply fractal theory to document summarization. It significantly improves the divergence of information coverage of summary and the precision of summary. User evaluations have been conducted. Results have indicated that fractal summarization is promising and outperforms current summarization techniques that do not consider the hierarchical structure of documents.
Themenfeld: Automatisches Abstracting
12He, W. ; Erdelez, S. ; Wang, F.-K. ; Shyu, C.-R.: ¬The effects of conceptual description and search practice on users' mental models and information seeking in a case-based reasoning retrieval system.
In: Information processing and management. 44(2008) no.1, S.294-309.
Abstract: This paper reportes a study that investigated the effects of conceptual description and search practice on users' mental models and information seeking in a case-based reasoning retrieval (CBR) system with a best match search mechanism. This study also found examined how the presence of a mental model affects the users' search performance and satisfaction in this system. The results of this study revealed that the conceptual description and search practice treatments do not have significantly different effects on the types of user's mental models, search correctness, and search satisfaction. However, the search practice group spent significantly less time than the conceptual description group in finding the results. Qualitative analysis for the subjects' post mental models revealed that subjects in the conceptual description group seem to have more complete mental models of the best match system than those in the search practice group. This study also that subjects with the best match mental models have significantly higher search correctness and search result satisfaction than subjects without the best match mental models. However, the best match mental models do not guarantee less search time in finding the results. This study did not find a significant correlation among search time, search correctness and search satisfaction. The study concludes with suggestions for future research and implications for system developers who are interested in CBR retrieval systems.
Themenfeld: Case Based Reasoning
13Wang, F.L. ; Yang, C.C.: Mining Web data for Chinese segmentation.
In: Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1820-1837.
Abstract: Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words.
Anmerkung: Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
Themenfeld: Data Mining ; Computerlinguistik
14Wang, F.L. ; Yang, C.C.: ¬The impact analysis of language differences on an automatic multilingual text summarization system.
In: Journal of the American Society for Information Science and Technology. 57(2006) no.5, S.684-696.
Abstract: Based on the salient features of the documents, automatic text summarization systems extract the key sentences from source documents. This process supports the users in evaluating the relevance of the extracted documents returned by information retrieval systems. Because of this tool, efficient filtering can be achieved. Indirectly, these systems help to resolve the problem of information overloading. Many automatic text summarization systems have been implemented for use with different languages. It has been established that the grammatical and lexical differences between languages have a significant effect on text processing. However, the impact of the language differences on the automatic text summarization systems has not yet been investigated. The authors provide an impact analysis of language difference on automatic text summarization. It includes the effect on the extraction processes, the scoring mechanisms, the performance, and the matching of the extracted sentences, using the parallel corpus in English and Chinese as the tested object. The analysis results provide a greater understanding of language differences and promote the future development of more advanced text summarization techniques.
Anmerkung: Beitrag einer special topic section on multilingual information systems
Themenfeld: Multilinguale Probleme ; Referieren