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)
1Li, D. ; Wang, Y. ; Madden, A. ; Ding, Y. ; Sun, G.G. ; Zhang, N. ; Zhou, E.: Analyzing stock market trends using social media user moods and social influence.
In: Journal of the Association for Information Science and Technology. 70(2019) no.9, S.1000-1013.
Abstract: Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3-month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)-based Tencent finance-related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods-based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen-300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short-term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.
Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24173.
2Li, H. ; Wu, H. ; Li, D. ; Lin, S. ; Su, Z. ; Luo, X.: PSI: A probabilistic semantic interpretable framework for fine-grained image ranking.
In: Journal of the Association for Information Science and Technology. 69(2018) no.12, S.1488-1501.
Abstract: Image Ranking is one of the key problems in information science research area. However, most current methods focus on increasing the performance, leaving the semantic gap problem, which refers to the learned ranking models are hard to be understood, remaining intact. Therefore, in this article, we aim at learning an interpretable ranking model to tackle the semantic gap in fine-grained image ranking. We propose to combine attribute-based representation and online passive-aggressive (PA) learning based ranking models to achieve this goal. Besides, considering the highly localized instances in fine-grained image ranking, we introduce a supervised constrained clustering method to gather class-balanced training instances for local PA-based models, and incorporate the learned local models into a unified probabilistic framework. Extensive experiments on the benchmark demonstrate that the proposed framework outperforms state-of-the-art methods in terms of accuracy and speed.
Behandelte Form: Bilder
3Madalli, D.P. ; Chatterjee, U. ; Dutta, B.: ¬An analytical approach to building a core ontology for food.
In: Journal of documentation. 73(2017) no.1, S.123-144.
Abstract: Purpose The purpose of this paper is to demonstrate the construction of a core ontology for food. To construct the core ontology, the authors propose here an approach called, yet another methodology for ontology plus (YAMO+). The goal is to exhibit the construction of a core ontology for a domain, which can be further extended and converted into application ontologies. Design/methodology/approach To motivate the construction of the core ontology for food, the authors have first articulated a set of application scenarios. The idea is that the constructed core ontology can be used to build application-specific ontologies for those scenarios. As part of the developmental approach to core ontology, the authors have proposed a methodology called YAMO+. It is designed following the theory of analytico-synthetic classification. YAMO+ is generic in nature and can be applied to build core ontologies for any domain. Findings Construction of a core ontology needs a thorough understanding of the domain and domain requirements. There are various challenges involved in constructing a core ontology as discussed in this paper. The proposed approach has proven to be sturdy enough to face the challenges that the construction of a core ontology poses. It is observed that core ontology is amenable to conversion to an application ontology. Practical implications The constructed core ontology for domain food can be readily used for developing application ontologies related to food. The proposed methodology YAMO+ can be applied to build core ontologies for any domain. Originality/value As per the knowledge, the proposed approach is the first attempt based on the study of the state of the art literature, in terms of, a formal approach to the design of a core ontology. Also, the constructed core ontology for food is the first one as there is no such ontology available on the web for domain food.
Inhalt: Vgl.: http://dx.doi.org/10.1108/JD-02-2016-0015.
4Li, D. ; Luo, Z. ; Ding, Y. ; Tang, J. ; Sun, G.G.-Z. ; Dai, X. ; Du, J. ; Zhang, J. ; Kong, S.: User-level microblogging recommendation incorporating social influence.
In: Journal of the Association for Information Science and Technology. 68(2017) no.3, S.553-568.
Abstract: With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23681/full.
5Su, Z. ; Li, D. ; Li, H. ; Luo, X.: Boosting attribute recognition with latent topics by matrix factorization.
In: Journal of the Association for Information Science and Technology. 68(2017) no.7, S.1737-1750.
Abstract: Attribute-based approaches have recently attracted much attention in visual recognition tasks. These approaches describe images by using semantic attributes as the mid-level feature. However, low recognition accuracy becomes the biggest barrier that limits their practical applications. In this paper, we propose a novel framework termed Boosting Attribute Recognition (BAR) for the image recognition task. Our framework stems from matrix factorization, and can explore latent relationships from the aspect of attribute and image simultaneously. Furthermore, to apply our framework in large-scale visual recognition tasks, we present both offline and online learning implementation of the proposed framework. Extensive experiments on 3 data sets demonstrate that our framework achieves a sound accuracy of attribute recognition.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23827/full.
Behandelte Form: Bilder
6Madalli, D.P. ; Balaji, B.P. ; Sarangi, A.K.: Faceted ontological representation for a music domain : an editorial.
In: Knowledge organization. 42(2015) no.1, S.8-24.
Abstract: This paper proposes an analysis of faceted theory and of various knowledge organization approaches. Building upon the faceted theory of S.R. Ranganathan (1967), the paper intends to address the faceted classification approach applied to build domain ontologies. Based on this perspective, an ontology of a music domain has been analyzed that would serve as a case study. As classificatory ontologies are employed to represent the relationships of entities and objects on the web, the faceted approach is deemed as an effective means to help organize web content. While different knowledge organization systems are being employed to address the cluttered Web in different contexts and with various degrees of effectiveness, faceted ontologies have an enormous potential for addressing this issue by performing.
Inhalt: Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/ko_42_2015_1_b.pdf.
7Candela, L. ; Castelli, D. ; Manghi, P. ; Tani, A.: Data journals : a survey.
In: Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1747-1762.
(Advances in information science)
Abstract: Data occupy a key role in our information society. However, although the amount of published data continues to grow and terms such as data deluge and big data today characterize numerous (research) initiatives, much work is still needed in the direction of publishing data in order to make them effectively discoverable, available, and reusable by others. Several barriers hinder data publishing, from lack of attribution and rewards, vague citation practices, and quality issues to a rather general lack of a data-sharing culture. Lately, data journals have overcome some of these barriers. In this study of more than 100 currently existing data journals, we describe the approaches they promote for data set description, availability, citation, quality, and open access. We close by identifying ways to expand and strengthen the data journals approach as a means to promote data set access and exploitation.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23358/abstract.
8Li, D. ; Tang, J. ; Ding, Y. ; Shuai, X. ; Chambers, T. ; Sun, G. ; Luo, Z. ; Zhang, J.: Topic-level opinion influence model (TOIM) : an investigation using tencent microblogging.
In: Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2657-2673.
Abstract: Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23350/abstract.
Themenfeld: Data Mining
9Bhansali, D. ; Desai, H. ; Deulkar, K.: ¬A study of different ranking approaches for semantic search.
In: International journal of computer applications. 129(2015) no.5, S12-15.
Abstract: Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank  algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.
Inhalt: Vgl. auch: http://www.ijcaonline.org/research/volume129/number5/bhansali-2015-ijca-906896.pdf.
Themenfeld: Suchmaschinen ; Retrievalalgorithmen ; Semantisches Umfeld in Indexierung u. Retrieval
Objekt: SemRank ; HITS
10Satija, M.P. ; Madalli, D.P. ; Dutta, B.: Modes of growth of subjects.
In: Knowledge organization. 41(2014) no.3, S.195-204.
Abstract: We define knowledge as a system in a perpetually dynamic continuum. Knowledge grows as it is always fragmentary, though quantifying this growth is nearly impossible. Growth, inherent in the nature of knowledge, is natural, planned, and induced. S.R. Ranganathan elucidated the various modes of growth of subjects, viz. fission, lamination, loose assemblage, fusion, distillation, partial comprehensions, and subject bundles. The present study adds a few more modes of developments of subjects. We describe and fit these modes of growth in the framework of growth by specialization, interdisciplinary and multidisciplinary growths. We also examine emergence of online domains such as web directories and focus on possible modes of formation of such domains. The paper concludes that new modes may emerge in the future in consonance with the new research trends and ever-changing social needs.
Inhalt: Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/ko_41_2014_3_b.pdf.
11Madalli, D.P. ; Balaji, B.P. ; Sarangi, A.K.: Music domain analysis for building faceted ontological representation.
In: Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik. Würzburg : Ergon Verlag, 2014. S.289-296.
(Advances in knowledge organization; vol. 14)
Abstract: This paper describes to construct faceted ontologies for domain modeling. Building upon the faceted theory of S.R. Ranganathan (1967), the paper intends to address the faceted classification approach applied to build domain ontologies. As classificatory ontologies are employed to represent the relationships of entities and objects on the web, the faceted approach helps to analyze domain representation in an effective way for modeling. Based on this perspective, an ontology of the music domain has been analyzed that would serve as a case study.
Inhalt: Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/aiko_vol_14_2014_40.pdf.
12Shen, J. ; Yao, L. ; Li, Y. ; Clarke, M. ; Wang, L. ; Li, D.: Visualizing the history of evidence-based medicine : a bibliometric analysis.
In: Journal of the American Society for Information Science and Technology. 64(2013) no.10, S.2157-2172.
Abstract: The aim of this paper is to visualize the history of evidence-based medicine (EBM) and to examine the characteristics of EBM development in China and the West. We searched the Web of Science and the Chinese National Knowledge Infrastructure database for papers related to EBM. We applied information visualization techniques, citation analysis, cocitation analysis, cocitation cluster analysis, and network analysis to construct historiographies, themes networks, and chronological theme maps regarding EBM in China and the West. EBM appeared to develop in 4 stages: incubation (1972-1992 in the West vs. 1982-1999 in China), initiation (1992-1993 vs. 1999-2000), rapid development (1993-2000 vs. 2000-2004), and stable distribution (2000 onwards vs. 2004 onwards). Although there was a lag in EBM initiation in China compared with the West, the pace of development appeared similar. Our study shows that important differences exist in research themes, domain structures, and development depth, and in the speed of adoption between China and the West. In the West, efforts in EBM have shifted from education to practice, and from the quality of evidence to its translation. In China, there was a similar shift from education to practice, and from production of evidence to its translation. In addition, this concept has diffused to other healthcare areas, leading to the development of evidence-based traditional Chinese medicine, evidence-based nursing, and evidence-based policy making.
13Tani, A. ; Candela, L. ; Castelli, D.: Dealing with metadata quality : the legacy of digital library efforts.
In: Information processing and management. 49(2013) no.6, S.1194-1205.
Abstract: In this work, we elaborate on the meaning of metadata quality by surveying efforts and experiences matured in the digital library domain. In particular, an overview of the frameworks developed to characterize such a multi-faceted concept is presented. Moreover, the most common quality-related problems affecting metadata both during the creation and the aggregation phase are discussed together with the approaches, technologies and tools developed to mitigate them. This survey on digital library developments is expected to contribute to the ongoing discussion on data and metadata quality occurring in the emerging yet more general framework of data infrastructures.
Inhalt: Vgl.: doi: 10.1016/j.ipm.2013.05.003.
14Lin, N. ; Li, D. ; Ding, Y. ; He, B. ; Qin, Z. ; Tang, J. ; Li, J. ; Dong, T.: ¬The dynamic features of Delicious, Flickr, and YouTube.
In: Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.139-162.
Abstract: This article investigates the dynamic features of social tagging vocabularies in Delicious, Flickr, and YouTube from 2003 to 2008. Three algorithms are designed to study the macro- and micro-tag growth as well as the dynamics of taggers' activities, respectively. Moreover, we propose a Tagger Tag Resource Latent Dirichlet Allocation (TTR-LDA) model to explore the evolution of topics emerging from those social vocabularies. Our results show that (a) at the macro level, tag growth in all the three tagging systems obeys power law distribution with exponents lower than 1; at the micro level, the tag growth of popular resources in all three tagging systems follows a similar power law distribution; (b) the exponents of tag growth vary in different evolving stages of resources; (c) the growth of number of taggers associated with different popular resources presents a feature of convergence over time; (d) the active level of taggers has a positive correlation with the macro-tag growth of different tagging systems; and (e) some topics evolve into several subtopics over time while others experience relatively stable stages in which their contents do not change much, and certain groups of taggers continue their interests in them.
Themenfeld: Social tagging
Objekt: Delicious ; Flickr ; YouTube
15Sugimoto, C.R. ; Li, D. ; Russell, T.G. ; Finlay, S.C. ; Ding, Y.: ¬The shifting sands of disciplinary development : analyzing North American Library and Information Science dissertations using latent Dirichlet allocation.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.1, S.185-204.
Abstract: This work identifies changes in dominant topics in library and information science (LIS) over time, by analyzing the 3,121 doctoral dissertations completed between 1930 and 2009 at North American Library and Information Science programs. The authors utilize latent Dirichlet allocation (LDA) to identify latent topics diachronically and to identify representative dissertations of those topics. The findings indicate that the main topics in LIS have changed substantially from those in the initial period (1930-1969) to the present (2000-2009). However, some themes occurred in multiple periods, representing core areas of the field: library history occurred in the first two periods; citation analysis in the second and third periods; and information-seeking behavior in the fourth and last period. Two topics occurred in three of the five periods: information retrieval and information use. One of the notable changes in the topics was the diminishing use of the word library (and related terms). This has implications for the provision of doctoral education in LIS. This work is compared to other earlier analyses and provides validation for the use of LDA in topic analysis of a discipline.
Wissenschaftsfach: Bibliothekswesen ; Informationswissenschaft
Behandelte Form: Dissertationen
16Petrelli, D. ; Lanfranchi, V. ; Ciravegna, F. ; Begdev, R. ; Chapman, S.: Highly focused document retrieval in aerospace engineering : user interaction design and evaluation.
In: Aslib proceedings. 63(2011) nos.2/3, S.148-167.
Abstract: Purpose - This paper seeks to describe the preliminary studies (on both users and data), the design and evaluation of the K-Search system for searching legacy documents in aerospace engineering. Real-world reports of jet engine maintenance challenge the current indexing practice, while real users' tasks require retrieving the information in the proper context. K-Search is currently in use in Rolls-Royce plc and has evolved to include other tools for knowledge capture and management. Design/methodology/approach - Semantic Web techniques have been used to automatically extract information from the reports while maintaining the original context, allowing a more focused retrieval than with more traditional techniques. The paper combines semantic search with classical information retrieval to increase search effectiveness. An innovative user interface has been designed to take advantage of this hybrid search technique. The interface is designed to allow a flexible and personal approach to searching legacy data. Findings - The user evaluation showed that the system is effective and well received by users. It also shows that different people look at the same data in different ways and make different use of the same system depending on their individual needs, influenced by their job profile and personal attitude. Research limitations/implications - This study focuses on a specific case of an enterprise working in aerospace engineering. Although the findings are likely to be shared with other engineering domains (e.g. mechanical, electronic), the study does not expand the evaluation to different settings. Originality/value - The study shows how real context of use can provide new and unexpected challenges to researchers and how effective solutions can then be adopted and used in organizations.
17Li, D. ; Ding, Y. ; Sugimoto, C. ; He, B. ; Tang, J. ; Yan, E. ; Lin, N. ; Qin, Z. ; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1849-1866.
Abstract: The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.
Themenfeld: Social tagging
18Madalli, D.P. ; Prasad, A.R.D.: Analytico-synthetic approach for handling knowledge diversity in media content analysis.
In: Classification and ontology: formal approaches and access to knowledge: proceedings of the International UDC Seminar, 19-20 September 2011, The Hague, The Netherlands. Eds.: A. Slavic u. E. Civallero. Würzburg : Ergon Verlag, 2011. S.229-239.
Abstract: Knowledge space is diverse and thus extremely complex. With increased means for online publishing and communication world communities are actively contributing content. This augments the need to find and access resources in different contexts and for different purposes. Owing to different socio-cultural backgrounds, purposes and applications, knowledge generated by people is marked by diversity. Hence, knowledge representation for building diversity-aware tools presents interesting research challenges. In this paper, we provide an analytico-synthetic approach for dealing with topical diversity following a faceted subject indexing method. Illustrations are used to demonstrate facet analysis and synthesis for use in annotations for Media Content Analysis within the European Commission (EC) funded 'Living Knowledge' project.
Themenfeld: Universale Facettenklassifikationen
Behandelte Form: Nonbook-Materialien
19Li, D. ; Kwong, C.-P.: Understanding latent semantic indexing : a topological structure analysis using Q-analysis.
In: Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.592-608.
Abstract: The method of latent semantic indexing (LSI) is well-known for tackling the synonymy and polysemy problems in information retrieval; however, its performance can be very different for various datasets, and the questions of what characteristics of a dataset and why these characteristics contribute to this difference have not been fully understood. In this article, we propose that the mathematical structure of simplexes can be attached to a term-document matrix in the vector space model (VSM) for information retrieval. The Q-analysis devised by R.H. Atkin () may then be applied to effect an analysis of the topological structure of the simplexes and their corresponding dataset. Experimental results of this analysis reveal that there is a correlation between the effectiveness of LSI and the topological structure of the dataset. By using the information obtained from the topological analysis, we develop a new method to explore the semantic information in a dataset. Experimental results show that our method can enhance the performance of VSM for datasets over which LSI is not effective.
Objekt: Latent Semantic Indexing ; Q-analysis
20Li, D. ; Kwong, C.-P. ; Lee, D.L.: Unified linear subspace approach to semantic analysis.
In: Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.175-189.
Abstract: The Basic Vector Space Model (BVSM) is well known in information retrieval. Unfortunately, its retrieval effectiveness is limited because it is based on literal term matching. The Generalized Vector Space Model (GVSM) and Latent Semantic Indexing (LSI) are two prominent semantic retrieval methods, both of which assume there is some underlying latent semantic structure in a dataset that can be used to improve retrieval performance. However, while this structure may be derived from both the term space and the document space, GVSM exploits only the former and LSI the latter. In this article, the latent semantic structure of a dataset is examined from a dual perspective; namely, we consider the term space and the document space simultaneously. This new viewpoint has a natural connection to the notion of kernels. Specifically, a unified kernel function can be derived for a class of vector space models. The dual perspective provides a deeper understanding of the semantic space and makes transparent the geometrical meaning of the unified kernel function. New semantic analysis methods based on the unified kernel function are developed, which combine the advantages of LSI and GVSM. We also prove that the new methods are stable because although the selected rank of the truncated Singular Value Decomposition (SVD) is far from the optimum, the retrieval performance will not be degraded significantly. Experiments performed on standard test collections show that our methods are promising.
Themenfeld: Semantisches Umfeld in Indexierung u. Retrieval
Objekt: Latent Semantic Indexing ; Generalized Vector Space Model