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)
1Xu, B. ; Lin, H. ; Lin, Y.: Assessment of learning to rank methods for query expansion.
In: Journal of the Association for Information Science and Technology. 67(2016) no.6, S.1345-1357.
Abstract: Pseudo relevance feedback, as an effective query expansion method, can significantly improve information retrieval performance. However, the method may negatively impact the retrieval performance when some irrelevant terms are used in the expanded query. Therefore, it is necessary to refine the expansion terms. Learning to rank methods have proven effective in information retrieval to solve ranking problems by ranking the most relevant documents at the top of the returned list, but few attempts have been made to employ learning to rank methods for term refinement in pseudo relevance feedback. This article proposes a novel framework to explore the feasibility of using learning to rank to optimize pseudo relevance feedback by means of reranking the candidate expansion terms. We investigate some learning approaches to choose the candidate terms and introduce some state-of-the-art learning to rank methods to refine the expansion terms. In addition, we propose two term labeling strategies and examine the usefulness of various term features to optimize the framework. Experimental results with three TREC collections show that our framework can effectively improve retrieval performance.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23476/abstract.
Themenfeld: Semantisches Umfeld in Indexierung u. Retrieval ; Retrievalalgorithmen
2Sun, 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
3Lin, 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.
4Hu, G. ; Lin, H. ; Pan, W.: Conceptualizing and examining E-government service capability : a review and empirical study.
In: Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2379-2395.
Abstract: The effectiveness and efficiency of e-government (e-gov) services (EGS) are critical issues that have yet to be fully discussed. Inspired by successful practices in the areas of SERVQUAL, capability-based theories, and IT-related capability management, the efficient delivery of EGS should derive from the high capabilities of a government to provide such services. This article aims to develop a conceptual framework to assess and empirically examine EGSC using data from local governments in Mainland China. The fitness test and the case study prove that the conceptual framework was suitable in analyzing China's EGSC. In particular, the EGSC can be examined from 3 dimensions/layers: content service capability, service delivery capability, and on-demand capability. The results of the structural analysis illustrate the practical management applications of EGSC, which can facilitate the improvement of EGS.
5Ye, Z. ; Huang, J.X. ; He, B. ; Lin, H.: Mining a multilingual association dictionary from Wikipedia for cross-language information retrieval.
In: Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2474-2487.
Abstract: Wikipedia is characterized by its dense link structure and a large number of articles in different languages, which make it a notable Web corpus for knowledge extraction and mining, in particular for mining the multilingual associations. In this paper, motivated by a psychological theory of word meaning, we propose a graph-based approach to constructing a cross-language association dictionary (CLAD) from Wikipedia, which can be used in a variety of cross-language accessing and processing applications. In order to evaluate the quality of the mined CLAD, and to demonstrate how the mined CLAD can be used in practice, we explore two different applications of the mined CLAD to cross-language information retrieval (CLIR). First, we use the mined CLAD to conduct cross-language query expansion; and, second, we use it to filter out translation candidates with low translation probabilities. Experimental results on a variety of standard CLIR test collections show that the CLIR retrieval performance can be substantially improved with the above two applications of CLAD, which indicates that the mined CLAD is of sound quality.
Themenfeld: Multilinguale Probleme
6Ye, Z. ; Huang, J.X. ; Lin, H.: Finding a good query-related topic for boosting pseudo-relevance feedback.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.748-760.
Abstract: Pseudo-relevance feedback (PRF) via query expansion (QE) assumes that the top-ranked documents from the first-pass retrieval are relevant. The most informative terms in the pseudo-relevant feedback documents are then used to update the original query representation in order to boost the retrieval performance. Most current PRF approaches estimate the importance of the candidate expansion terms based on their statistics on document level. However, a document for PRF may consist of different topics, which may not be all related to the query even if the document is judged relevant. The main argument of this article is the proposal to conduct PRF on a granularity smaller than on the document level. In this article, we propose a topic-based feedback model with three different strategies for finding a good query-related topic based on the Latent Dirichlet Allocation model. The experimental results on four representative TREC collections show that QE based on the derived topic achieves statistically significant improvements over a strong feedback model in the language modeling framework, which updates the query representation based on the top-ranked documents.
7Wu, I.-L. ; Lin, H.-C.: ¬A strategy-based process for implementing knowledge management : an integrative view and empirical study.
In: Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.789-802.
Abstract: Knowledge resource is unique and valuable for a link to competitive advantage based on the knowledge-based perspective. Effective knowledge management is the major concern of contemporary business managers. The key determinant of effective knowledge management is the firm's competitive strategy. The link between business strategy and knowledge management, while often discussed, has been widely ignored in practice. Moreover, while knowledge management is complex in nature, it is difficult to directly translate a firm's competitive strategy into the specific knowledge management activities. This requires first defining knowledge strategy to guide further information technology (IT)-supported implementation approaches. Finally, the ultimate goal of knowledge management lies in the realization of firm performance. Previous studies have just discussed partial relationship among these relevant knowledge concepts rather than in an integrative manner. Thus, this research proposes a complete process-based model with four components: competitive strategy, knowledge strategy, implementation approach, and firm performance. Empirical results have shown positive relationships between any two consecutive components and useful insight for knowledge implementation practice.
8Penzlin, H.: ¬Die Welt als Täuschung : Wahrnehmung.
In: Gehirn und Geist: Das Magazin für Hirnforschung und Psychologie. 2002, H.3, S.68-73.
Abstract: Wie nehmen wir die Welt wahr? Forscher antworten: Nicht wie sie wirklich ist, sondern so, dass wir jeden Tag aufs Neue überleben können, jetzt entschlüsseln sie, wie das Bild, das unser Gehirn konstruiert, mit der realen Welt zusammenhängt
Inhalt: Mit Abbildungen: (1) Ponzo-Täuschung (2) Funktionsweise des Auges (3) Verschiedene Modelle des Realismus