Search (12 results, page 1 of 1)

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Source

Annual review of information science and technology. 22(1987), S.227-263

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Abstract

Intelligent agents should be used to assist users of the WWW. Identifies a number of key components in such a system and proposes a system architecture. Designs a learning agent along with the underlying algorithms for the discovery of areas of interest from user access logs. The discovered topics can be used to improve the efficiency of information retrieval by prefetching documents for the users and storing them in a document database in the system. Implements a prototype system

Footnote

Contribution to a special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997

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Abstract

On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.

Source

Information processing and management. 50(2014) no.1, S.132-155

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Abstract

In a big data environment, there are growing concerns about the violation of consumer rights regarding information privacy. To induce rational regulations for protecting personal information, it is necessary to separately estimate consumers' values related to different types of personal information. In this article, discrete choice experiments using hypothetical information leakage situations given certain compensation amounts and discrete choice models were used to quantitatively analyze the value of personal information. The results indicate that consumers generally place high value on information that could cause immediate and actual damage from the leakage after identification, such as basic personal information and purchase list and payment information. Consumers value location information and personal medical information differently based on their perceived importance of privacy and their prior experience with personal information leakage. We suggest that the level of regulation should differ according to the type of personal information based on the consumers' valuation. This article contributes to a better understanding of a quantitative approach to pricing personal information.

Source

Journal of the Association for Information Science and Technology. 69(2018) no.1, S.60-71

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Source

Encyclopedia of library and information science. Vol.62, [=Suppl.25]

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Abstract

Recent developments in geographical information systems (GIS) have dramatically advanced the ways in which geographical information and data is stored, manipulated, analyzed and displayed. Architectural librarians can now use GIS to manage efficiently their maps and graphic illustrations of architectural designs with computerized procedures

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Abstract

The increasing spread of Internet technology has highlighted the need for a better understanding of the fundamental issues concerning human users in a virtual space. Despite the great degree of navigational freedom, however, not all hypermedia users have the capability to locate information or assimilate internal knowledge. Research findings suggest that this type of problem could be solved if users were able to hold a cognitive overview of the hypermedia structure. How a learner can acquire the correct structural knowledge of online information has become an important factor in learning performance in a hypermedia environment. Variables that might influence learners' abilities in structuring a cognitive overview, such as users' cognitive styles and the different ways of representing information, should be carefully taken into account. The results of this study show that the interactions between information representation approaches and learners' cognitive styles have significant effects on learners' performance in terms of structural knowledge and feelings of disorientation. Learners' performance could decline if a representational approach that contradicts their cognitive style is used. Finally, the results of the present study may apply only when the learner's knowledge level is in the introductory stage. It is not clear how and what type of cognitive styles, as well as information representation approaches, will affect the performance of advanced and expert learners.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.4, S.644-661

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Abstract

The rapid and wide dissemination of up-to-date, localized information is a central issue during disasters. Being attributed to the original 140-character length, Twitter provides its users with quick-posting and easy-forwarding features that facilitate the timely dissemination of warnings and alerts. However, a concern arises with respect to the terseness of tweets that restricts the amount of information conveyed in a tweet and thus increases a tweet's uncertainty. We tackle such concerns by proposing entropy as a measure for a tweet's uncertainty. Based on the perspectives of Uncertainty Reduction Theory (URT), we theorize that the more uncertain information of a disaster tweet, the higher the entropy, which will lead to a lower retweet count. By leveraging the statistical and predictive analyses, we provide evidence supporting that entropy validly and reliably assesses the uncertainty of a tweet. This study contributes to improving our understanding of information propagation on Twitter during disasters. Academically, we offer a new variable of entropy to measure a tweet's uncertainty, an important factor influencing disaster tweets' retweeting. Entropy plays a critical role to better comprehend URLs and emoticons as a means to convey information. Practically, this research suggests a set of guidelines for effectively crafting disaster messages on Twitter.

Source

Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1145-1161

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Abstract

This study aims to identify the way researchers collaborate with other researchers in the course of the scientific research life cycle and provide information to the designers of e-Science and e-Research implementations. On the basis of in-depth interviews with and on-site observations of 24 scientists and a follow-up focus group interview in the field of bioscience/nanoscience and technology in Korea, we examined scientific collaboration using the framework of the scientific research life cycle. We attempt to explain the major motivations, characteristics of communication and information sharing, and barriers associated with scientists' research collaboration practices throughout the research life cycle. The findings identify several notable phenomena including motivating factors, the timing of collaboration formation, partner selection, communication methods, information-sharing practices, and barriers at each phase of the life cycle. We find that specific motivations were related to specific phases. The formation of collaboration was observed throughout the entire process, not only in the beginning phase of the cycle. For communication and information-sharing practices, scientists continue to favor traditional means of communication for security reasons. Barriers to collaboration throughout the phases included different priorities, competitive tensions, and a hierarchical culture among collaborators, whereas credit sharing was a barrier in the research product phase.

Source

Journal of the Association for Information Science and Technology. 67(2016) no.8, S.1836-1848

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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.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.6, S.563-574

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Abstract

Analyzing social media has become a common way for capturing and understanding people's opinions, sentiments, interests, and reactions to ongoing events. Social media has thus become a rich and real-time source for various kinds of public opinion and sentiment studies. According to psychology and neuroscience, human emotions are known to be strongly dependent on sensory perceptions. Although sensation is the most fundamental antecedent of human emotions, prior works have not looked into their relation to emotions based on social media texts. In this paper, we report the results of our study on sensation effects that underlie human emotions as revealed in social media. We focus on the key five types of sensations: sight, hearing, touch, smell, and taste. We first establish a correlation between emotion and sensation in terms of linguistic expressions. Then, in the second part of the paper, we define novel features useful for extracting sensation information from social media. Finally, we design a method to classify texts into ones associated with different types of sensations. The sensation dataset resulting from this research is opened to the public to foster further studies.

Source

Journal of the Association for Information Science and Technology. 72(2021) no.4, S.417-432

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Source

Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1065-1078