Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
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1Roy, D. ; Bhatia, S. ; Jain, P.: Information asymmetry in Wikipedia across different languages : a statistical analysis.
In: Journal of the Association for Information Science and Technology. 73(2022) no.3, S.347-361.
Abstract: Wikipedia is the largest web-based open encyclopedia covering more than 300 languages. Different language editions of Wikipedia differ significantly in terms of their information coverage. In this article, we compare the information coverage in English Wikipedia (most exhaustive) and Wikipedias in 8 other widely spoken languages, namely Arabic, German, Hindi, Korean, Portuguese, Russian, Spanish, and Turkish. We analyze variations in different language editions of Wikipedia in terms of the number of topics covered as well as the amount of information discussed about different topics. Further, as a step towards bridging the information gap, we present WikiCompare-a browser plugin that allows Wikipedia readers to have a comprehensive overview of topics by incorporating missing information from Wikipedia page in other language.
Inhalt: Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24553.
Themenfeld: Multilinguale Probleme
2Bhatia, S. ; Biyani, P. ; Mitra, P.: Identifying the role of individual user messages in an online discussion and its use in thread retrieval.
In: Journal of the Association for Information Science and Technology. 67(2016) no.2, S.276-288.
Abstract: Online discussion forums have become a popular medium for users to discuss with and seek information from other users having similar interests. A typical discussion thread consists of a sequence of posts posted by multiple users. Each post in a thread serves a different purpose providing different types of information and, thus, may not be equally useful for all applications. Identifying the purpose and nature of each post in a discussion thread is thus an interesting research problem as it can help in improving information extraction and intelligent assistance techniques. We study the problem of classifying a given post as per its purpose in the discussion thread and employ features based on the post's content, structure of the thread, behavior of the participating users, and sentiment analysis of the post's content. We evaluate our approach on two forum data sets belonging to different genres and achieve strong classification performance. We also analyze the relative importance of different features used for the post classification task. Next, as a use case, we describe how the post class information can help in thread retrieval by incorporating this information in a state-of-the-art thread retrieval model.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23373/abstract.
3Bhatia, S.K. ; Deogun, J.S. ; Raghavan, V.V.: Conceptual query formulation and retrieval.
In: Journal of intelligent information systems. 5(1995) no.3, S.183-209.
Abstract: In this paper, we advance a technique to develop a user profile for information retrieval through knowledge acquisition techniques. The profile bridges the discrepancy between user-expressed keywords and system-recognizable index terms. The approach presented in this paper is based on the application of personal construct theory to determine a user's vocabulary and his/her view of different documents in a training set. The elicited knowledge is used to develop a model for each phrase/concept given by the user by employing machine learning techniques. Our model correlates the concepts in a user's vocabulary to the index terms present in the documents in the training set. Computation of dependence between the user phrases also contributes in the development of the user profile and in creating a classification of documents. The resulting system is capable of automatically identifying the user concepts and query translation to index terms computed by the conventional indexing process. The system is evaluated by using the standard measures of precision and recall by comparing its performance against the performance of the smart system for different queries.