Search (20 results, page 1 of 1)

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Abstract

Precision One MediaSource First Edition is the first film and video listing on CD-ROM containing bibliographic records and information about renatl sources. It was co-produced by the Brodart Co., Pennsylvania, and the Consortium of College and University Media Centres (CCUMC) and requires an IBM/campatible with hard disk, CD-ROM drive and DOS 3.3 or higher. MediaSource is intended for educational and business users and is of particular interest to public, school and academic libraries. Discusses installation, the interface and searching, data quality and documentation

Date

22. 6.1997 16:34:51

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Abstract

Purpose Online knowledge communities make great contributions to global knowledge sharing and innovation. Resource tagging approaches have been widely adopted in such communities to describe, annotate and organize knowledge resources mainly through users' participation. However, it is unclear what causes the adoption of a particular resource tagging approach. The purpose of this paper is to identify factors that drive users to use a hybrid social tagging approach. Design/methodology/approach Technology acceptance model and social cognitive theory are adopted to support an integrated model proposed in this paper. Zhihu, one of the most popular online knowledge communities in China, is taken as the survey context. A survey was conducted with a questionnaire and collected data were analyzed through structural equation model. Findings A new hybrid social resource tagging approach was refined and described. The empirical results revealed that self-efficacy, perceived usefulness (PU) and perceived ease of use exert positive effect on users' attitude. Moreover, social influence, PU and attitude impact significantly on users' intention to use a hybrid social resource tagging approach. Originality/value Theoretically, this study enriches the type of resource tagging approaches and recognizes factors influencing user adoption to use it. Regarding the practical parts, the results provide online information system providers and designers with referential strategies to improve the performance of the current tagging approaches and promote them.

Date

20. 1.2015 18:30:22

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Abstract

Name ambiguity in the context of bibliographic citation affects the quality of services in digital libraries. Previous methods are not widely applied in practice because of their high computational complexity and their strong dependency on excessive attributes, such as institutional affiliation, research area, address, etc., which are difficult to obtain in practice. To solve this problem, we propose a novel coarse-to-fine framework for name disambiguation which sequentially employs 3 common and easily accessible attributes (i.e., coauthor name, article title, and publication venue). Our proposed framework is based on multiple clustering and consists of 3 steps: (a) clustering articles by coauthorship and obtaining rough clusters, that is fragments; (b) clustering fragments obtained in step 1 by title information and getting bigger fragments; (c) and clustering fragments obtained in step 2 by the latent relations among venues. Experimental results on a Digital Bibliography and Library Project (DBLP) data set show that our method outperforms the existing state-of-the-art methods by 2.4% to 22.7% on the average pairwise F1 score and is 10 to 100 times faster in terms of execution time.

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Abstract

Purpose - This paper aims to provide a new insight into the reasons why authors cite. Design/methodology/approach The authors argue that, based on philosophical ideas about the essence of things, pure rational thinking about the role of citations leads to the answer. Findings - Citations originate from the interestingness of the investigated phenomenon. The essence of citation lies in the interaction between different ideas or perspectives on a phenomenon addressed in the citing as well as in the cited articles. Research limitations/implications - The findings only apply to ethical (not whimsical or self-serving) citations. As such citations reflect interactions of scientific ideas, they can reveal the evolution of science, revive the cognitive process of an investigated scientific phenomenon and reveal political and economic factors influencing the development of science. Originality/value - This article is the first to propose interestingness and the interaction of ideas as the basic reason for citing. This view on citations allows reverse engineering from citations to ideas and hence becomes useful for science policy.

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Abstract

With the development of Web-based social networks, many personalized tag recommendation approaches based on multi-information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user-item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real-world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.

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Abstract

The number of digital immigrants using new technologies such as smartphones is rapidly increasing. However, digital immigrants still struggle to actually use and benefit from digital technology. This article examines the role of family support in digital immigrants' use of more smartphone functions based on information technology (IT) identity theory. We surveyed 241 digital immigrants who owned smartphones and used structural equation modeling (PLS-SEM) for analysis. We examined the contributing roles of family support for digital immigrants' IT identity and extended use behavior. Family cognitive and emotional support can shape IT identity by improving the smartphone-related experience. Family support has a positive impact on digital immigrants' self-efficacy, embeddedness, perceived usefulness, and perceived enjoyment of using a smartphone. Positive usage experience can also facilitate the establishment of IT identity, which is a key predictor of smartphone use behavior. A strong IT identity also promotes extended use behavior. We discuss the contributions and implications of our findings.

Content

Beitrag in: JASIST special issue on ICT4D and intersections with the information field. Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24747.

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Abstract

Understanding what kinds of Web pages are the most useful for Web search engine users is a critical task in Web information retrieval (IR). Most previous works used hyperlink analysis algorithms to solve this problem. However, little research has been focused on query-independent Web data cleansing for Web IR. In this paper, we first provide analysis of the differences between retrieval target pages and ordinary ones based on more than 30 million Web pages obtained from both the Text Retrieval Conference (TREC) and a widely used Chinese search engine, SOGOU (www.sogou.com). We further propose a learning-based data cleansing algorithm for reducing Web pages that are unlikely to be useful for user requests. We found that there exists a large proportion of low-quality Web pages in both the English and the Chinese Web page corpus, and retrieval target pages can be identified using query-independent features and cleansing algorithms. The experimental results showed that our algorithm is effective in reducing a large portion of Web pages with a small loss in retrieval target pages. It makes it possible for Web IR tools to meet a large fraction of users' needs with only a small part of pages on the Web. These results may help Web search engines make better use of their limited storage and computation resources to improve search performance.

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Abstract

Role analysis plays an important role when exploring social media and knowledge-sharing platforms for designing marking strategies. However, current methods in role analysis have overlooked content generated by users (e.g., posts) in social media and hence focus more on user behavior analysis. The user-generated content is very important for characterizing users. In this paper, we propose a novel method which integrates both user behavior and posted content by users to identify roles in online social networks. The proposed method models a role as a joint distribution of Gaussian distribution and multinomial distribution, which represent user behavioral feature and content feature respectively. The proposed method can be used to determine the number of roles concerned automatically. The experimental results show that the proposed method can be used to identify various roles more effectively and to get more insights on such characteristics.

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Abstract

With the advent of various services and applications of Semantic Web, semantic annotation has emerged as an important research topic. The application of semantically annotated ontology had been evident in numerous information processing and retrieval tasks. One of such tasks is utilizing the semantically annotated ontology in product design which is able to suggest many important applications that are critical to aid various design related tasks. However, ontology development in design engineering remains a time consuming and tedious task that demands considerable human efforts. In the context of product family design, management of different product information that features efficient indexing, update, navigation, search and retrieval across product families is both desirable and challenging. For instance, an efficient way of retrieving timely information on product family can be useful for tasks such as product family redesign and new product variant derivation when requirements change. However, the current research and application of information search and navigation in product family is mostly limited to its structural aspect which is insufficient to handle advanced information search especially when the query targets at multiple aspects of a product. This paper attempts to address this problem by proposing an information search and retrieval framework based on the semantically annotated multi-facet product family ontology. Particularly, we propose a document profile (DP) model to suggest semantic tags for annotation purpose. Using a case study of digital camera families, we illustrate how the faceted search and retrieval of product information can be accomplished. We also exemplify how we can derive new product variants based on the designer's query of requirements via the faceted search and retrieval of product family information. Lastly, in order to highlight the value of our current work, we briefly discuss some further research and applications in design decision support, e.g. commonality analysis and variety comparison, based on the semantically annotated multi-facet product family ontology.

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Abstract

Using citation data of articles written by some Nobel Prize winners in physics, we show that concave, convex, and straight curves represent different types of interactions between old ideas and new insights. These cases illustrate different diffusion characteristics of academic knowledge, depending on the nature of the knowledge in the new publications. This work adds to the study of the development of science and links this development to citation analysis.

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Abstract

With more and more information available on the Internet, the task of making personalized recommendations to assist the user's navigation has become increasingly important. Considering there might be millions of users with different backgrounds accessing a Web site everyday, it is infeasible to build a separate recommendation system for each user. To address this problem, clustering techniques can first be employed to discover user groups. Then, user navigation patterns for each group can be discovered, to allow the adaptation of a Web site to the interest of each individual group. In this paper, we propose to model user access sequences as stochastic processes, and a mixture of Markov models based approach is taken to cluster users and to capture the sequential relationships inherent in user access histories. Several important issues that arise in constructing the Markov models are also addressed. The first issue lies in the complexity of the mixture of Markov models. To improve the efficiency of building/maintaining the mixture of Markov models, we develop a lightweight adapt-ive algorithm to update the model parameters without recomputing model parameters from scratch. The second issue concerns the proper selection of training data for building the mixture of Markov models. We investigate two different training data selection strategies and perform extensive experiments to compare their effectiveness on a real dataset that is generated by a Web-based knowledge management system, Livelink.

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Abstract

Prior research has discovered that groups tend to discuss shared information while failing to discuss unique information in decision-making processes. In our study, we conducted a lab experiment to examine the effect of prediscussion note-taking on this phenomenon. The experiment used a murder-mystery hidden profile task. In all, 192 undergraduate students were recruited and randomly assigned into 48 four-person groups with gender being the matching variable (i.e., each group consisted of four same-gender participants). During the decision-making processes, some groups were asked to take notes while reading task materials and had their notes available in the following group discussion, while the other groups were not given this opportunity. Our analysis results suggest that (a) the presence of an information piece in group members' notes positively correlates with its appearance in the subsequent discussion and note-taking positively affects the group's information repetition rate; (b) group decision quality positively correlates with the group's information sampling rate and negatively correlates with the group's information sampling/repetition bias; and (c) gender has no statistically significant moderating effect on the relationship between note-taking and information sharing. These results imply that prediscussion note-taking could facilitate information sharing but could not alleviate the biased information pooling in hidden profile tasks.

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Abstract

Two general challenges faced by data analysis are the existence of noise and the extraction of meaningful information from collected data. In this study, we used a multiscale framework to reduce the effects caused by noise and to extract explainable geometric properties to characterize finite metric spaces. We conducted lab experiments that integrated the use of eye-tracking, electrodermal activity (EDA), and user logs to explore users' information-seeking behaviors on search engine result pages (SERPs). Experimental results of 1,590 search queries showed that the proposed strategies effectively predicted query-level user satisfaction using EDA and eye-tracking data. The bootstrap analysis showed that combining EDA and eye-tracking data with user behavior data extracted from user logs led to a significantly better linear model fit than using user behavior data alone. Furthermore, cross-user and cross-task validations showed that our methods can be generalized to different search engine users performing different preassigned tasks.

Footnote

Beitrag in einem 'Special issue on neuro-information science'.

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Abstract

Purpose Serendipitous information discovery has become a unique and important approach to discovering and obtaining information, which has aroused a growing interest for serendipity in human information behavior. Despite numerous publications, few have systematically provided an overview of current state of serendipity research. Consequently, researchers and practitioners are less able to make effective use of existing achievements, which limits them from making advancements in this domain. Against this backdrop, we performed a systematic literature review to explore the world of serendipity and to recapitulate the current states of different research topics. Design/methodology/approach Guided by a prior designed review protocol, this paper conducted both automatic and manual search for available studies published from January 1990 to December 2020 on seven databases. A total of 207 serendipity studies closely related to human information behavior form the literature pool. Findings We provide an overview of distinct aspects of serendipity, that is research topics, potential benefits, related concepts, theoretical models, contextual factors and data collection methods. Based on these findings, this review reveals limitations and gaps in the current serendipity research and proposes an agenda for future research directions. Originality/value By analyzing current serendipity research, developing a knowledge framework and providing a research agenda, this review is of significance for researchers who want to find new research questions or re-align current work, for beginners who need to quickly understand serendipity, and for practitioners who seek to cultivate serendipity in information environments.

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Abstract

Purpose - This paper aims to introduce a general framework for the analysis of knowledge integration and diffusion using bibliometric data. Design/methodology/approach - The authors propose that in order to characterise knowledge integration and diffusion of a given issue (the source, for example articles on a topic or by an organisation, etc.), one has to choose a set of elements from the source (the intermediary set, for example references, keywords, etc.). This set can then be classified into categories (cats), thus making it possible to investigate its diversity. The set can also be characterised according to the coherence of a network associated to it. Findings - This framework allows a methodology to be developed to assess knowledge integration and diffusion. Such methodologies can be useful for a number of science policy issues, including the assessment of interdisciplinarity in research and dynamics of research networks. Originality/value - The main contribution of this article is to provide a simple and easy to use generalisation of an existing approach to study interdisciplinarity, bringing knowledge integration and knowledge diffusion together in one framework.

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Abstract

Adverse Drug Reactions (ADRs) have become a serious health problem and even a leading cause of death in the United States. Pre-marketing clinical trials and traditional post-marketing surveillance using voluntary and spontaneous report systems are insufficient for ADR detection. On the other hand, online health forums provide valuable evidences in a large scale and in a timely fashion through the active participation of patients, caregivers, and doctors. In this article, we present patient-centered and experience-aware mining framework for effective ADR discovery using online health forum data. Our experimental evaluation with both an official ADR knowledge base and human-annotated ground truth verifies the effectiveness of the proposed method for ADR discovery.

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Abstract

Product family design is one of the prevailing approaches in realizing mass customization. With the increasing number of product offerings targeted at different market segments, the issue of information management in product family design, that is related to an efficient and effective storage, sharing and timely retrieval of design information, has become more complicated and challenging. Product family modelling schema reported in the literature generally stress the component aspects of a product family and its analysis, with a limited capability to model complex inter-relations between physical components and other required information in different semantic orientations, such as manufacturing, material and marketing wise. To tackle this problem, ontology-based representation has been identified as a promising solution to redesign product platforms especially in a semantically rich environment. However, ontology development in design engineering demands a great deal of time commitment and human effort to process complex information. When a large variety of products are available, particularly in the consumer market, a more efficient method for building a product family ontology with the incorporation of multi-faceted semantic information is therefore highly desirable. In this study, we propose a methodology for building a semantically annotated multi-faceted ontology for product family modelling that is able to automatically suggest semantically-related annotations based on the design and manufacturing repository. The six steps of building such ontology: formation of product family taxonomy; extraction of entities; faceted unit generation and concept identification; facet modelling and semantic annotation; formation of a semantically annotated multi-faceted product family ontology (MFPFO); and ontology validation and evaluation are discussed in detail. Using a family of laptop computers as an illustrative example, we demonstrate how our methodology can be deployed step by step to create a semantically annotated MFPFO. Finally, we briefly discuss future research issues as well as interesting applications that can be further pursued based on the MFPFO developed.

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Abstract

Two forms of diffusion are studied: diffusion by publications, originating from the fact that a group publishes in different fields; and diffusion by citations, originating from the fact that the group's publications are cited in different fields. The first form of diffusion originates from an internal mechanism by which the group itself expands its own borders. The second form is partly driven by an external mechanism, in the sense that other fields use or become interested in the original group's expertise, and partly by the group's internal dynamism, in the sense that their articles, being published in more and more fields, have the potential to be applied in these other fields. In this contribution, we focus on basic counting measures as measures of diffusion. We introduce the notions of field diffusion breadth, defined as the number of for Essential Science Indicators (ESI) fields in which a set of articles is cited, and field diffusion intensity, defined as the number of citing articles in one particular ESI field. Combined effects of publications and citations can be measured by the Gini evenness measure. Our approach is illustrated by a study of mathematics at Tongji University (Shanghai, China).

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Abstract

With the rapid development of the Internet and its applications, growing volumes of documents increasingly become interconnected to form large-scale document networks. Accordingly, topic modeling in a network of documents has been attracting continuous research attention. Most of the existing network-based topic models assume that topics in a document are influenced by its directly linked neighbouring documents in a document network and overlook the potential influence from indirectly linked ones. The existing work also has not carefully modeled variations of such influence among neighboring documents. Recognizing these modeling limitations, this paper introduces a novel Local Context-Aware LDA Model (LC-LDA), which is capable of observing a local context comprising a rich collection of documents that may directly or indirectly influence the topic distributions of a target document. The proposed model can also differentiate the respective influence of each document in the local context on the target document according to both structural and temporal relationships between the two documents. The proposed model is extensively evaluated through multiple document clustering and classification tasks conducted over several large-scale document sets. Evaluation results clearly and consistently demonstrate the effectiveness and superiority of the new model with respect to several state-of-the-art peer models.

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Abstract

Users increasingly acquire, share, and create knowledge in online knowledge communities, making them massive dynamic knowledge repositories that spark inspiration. While online knowledge communities provide powerful searching tools, they ignore the potential of serendipity in fostering knowledge acquisition. Against this backdrop, this research investigates whether serendipity can be stimulated by design features of communities. Specifically, we examine whether different tag presentation formats may promote serendipity. Two hundred seven participants were randomly assigned to our experimental website that displays one of three tag formats. Results show that users experienced serendipity more frequently while using tag trees than tag clouds, followed by tag lists. Moreover, tag formats moderate how openness to experience affects serendipity. Although openness did not influence serendipity across tag formats, further analysis shows that it significantly decreases serendipity for tag lists, but significantly increases serendipity for tag clouds and trees. Theoretically, these results provide an in-depth understanding of serendipity that is contingent on the interaction between community design features and personality (e.g., openness to experience). Practically, these findings demonstrate how interface features (e.g., tag presentation formats) facilitate serendipity, thus informing better design of online knowledge communities to improve the efficiency of knowledge acquisition.