Search (9 results, page 1 of 1)

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Abstract

Artificial intelligence (AI) is a multidisciplinary subject, typically studied as a research area within Computer Science. AI study aims at achieving a good understanding of the nature of intelligence and building intelligent agents which are computational systems demonstrating intelligent behavior. AI has been developed over more than 50 years. The topics studied in AI are quite broad, ranging from knowledge representation and reasoning, knowledge-based systems, machine learning and data mining, natural language processing, to search, image processing, robotics, and intelligent information systems. Numerous successful AI systems have been deployed in real-life applications in engineering, finance, science, health care, education, and service sectors. AI research has also significantly impacted the subject area of Library and Information Science (LIS), helping to develop smart Web search engines, personalized news filters, and knowledge-sharing and indexing systems. This entry briefly outlines the main topics studied in AI, samples some typical successful AI applications, and discusses the cross-fertilization between AI and LIS.

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Abstract

Ensuring Wikipedia cites scholarly publications based on quality and relevancy without biases is critical to credible and fair knowledge dissemination. We investigate gender- and country-based biases in Wikipedia citation practices using linked data from the Web of Science and a Wikipedia citation dataset. Using coarsened exact matching, we show that publications by women are cited less by Wikipedia than expected, and publications by women are less likely to be cited than those by men. Scholarly publications by authors affiliated with non-Anglosphere countries are also disadvantaged in getting cited by Wikipedia, compared with those by authors affiliated with Anglosphere countries. The level of gender- or country-based inequalities varies by research field, and the gender-country intersectional bias is prominent in math-intensive STEM fields. To ensure the credibility and equality of knowledge presentation, Wikipedia should consider strategies and guidelines to cite scholarly publications independent of the gender and country of authors.

Date

22. 1.2023 18:53:32

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Abstract

Human interpretation of images during image annotation is complicated, but most existing interactive image annotation systems are generally operated based on social tagging, while ignoring that tags are insufficient to convey image semantics. Hence, it is critical to study the nature of image annotation behaviors and process. This study investigated annotation tactics, transitions, strategies and their efficiency during the image annotation process. A total of 90 participants were recruited to annotate nine pictures in three emotional dimensions with three interactive annotation methods. Data collected from annotation logs and verbal protocols were analyzed by applying both qualitative and quantitative methods. The findings of this study show that the cognitive process of human interpretation of images is rather complex, which reveals a probable bias in research involving image relevance feedback. Participants preferred applying scroll bar (Scr) and image comparison (Cim) tactics comparing with rating tactic (Val), and they did fewer fine tuning activities, which reflects the influence of perceptual level and users' cognitive load during image annotation. Annotation tactic transition analysis showed that Cim was more likely to be adopted at the beginning of each phase, and the most remarkable transition was from Cim to Scr. By applying sequence analysis, the authors found 10 most commonly used sequences representing four types of annotation strategies, including Single tactic strategy, Tactic combination strategy, Fix mode strategy and Shift mode strategy. Furthermore, two patterns, "quarter decreasing" and "transition cost," were identified based on time data, and both multiple tactics (e.g., the combination of Cim and Scr) and fine tuning activities were recognized as efficient tactic applications. Annotation patterns found in this study suggest more research needs to be done considering the need for multi-interactive methods and their influence. The findings of this study generated detailed and useful guidance for the interactive design in image annotation systems, including recommending efficient tactic applications in different phases, highlighting the most frequently applied tactics and transitions, and avoiding unnecessary transitions.

Source

Information processing and management. 54(2018) no.6, S.985-1001

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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

Semantic mapping between controlled vocabulary and keywords is the first step towards knowledge-based subject access. This study reports the preliminary result of a semantic mapping experiment for the Gateway to Educational Materials (GEM). A total of 3,555 keywords were mapped with 322 concept names in the GEM controlled vocabulary. The preliminary test to 10,000 metadata records presented widely varied sets of results between the mapped and non-mapped data. The paper discussed linguistic and technical problems encountered in the mapping process and raised issues in the representation technologies and methods, which will lead to future study of knowledge-based access to networked information resources.

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Abstract

Knowledge withholding, which is defined as the likelihood that an individual devotes less than full effort to knowledge contribution, can be regarded as an emerging social deviance behavior for knowledge practice in online knowledge spaces. However, prior studies placed a great emphasis on proactive knowledge behaviors, such as knowledge sharing and contribution, but failed to consider the uniqueness of knowledge withholding. To capture the social-deviant nature of knowledge withholding and to better understand how people deal with counterproductive knowledge behaviors, this study develops a research model based on the secondary control perspective. Empirical analyses were conducted using the data collected from an online knowledge space. The results indicate that both predictive control and vicarious control exert a positive influence on knowledge withholding. This study also incorporates knowledge-withholding acceptability as a moderating variable of secondary control strategies. In particular, knowledge-withholding acceptability enhances the impact of predictive control, whereas it weakens the effect of vicarious control on knowledge withholding. This study concludes with a discussion of the key findings, and the implications for both research and practice.

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Abstract

Main path analysis is a famous network-based method for understanding the evolution of a scientific domain. Most existing methods have two steps, weighting citation arcs based on search path counting and exploring main paths in a greedy fashion, with the assumption that citation networks are acyclic. The only available proposal that avoids manual cycle removal is to preprint transform a cyclic network to an acyclic counterpart. Through a detailed discussion about the issues concerning this approach, especially deriving the "de-preprinted" main paths for the original network, this article proposes an alternative solution with two-fold contributions. Based on the argument that a publication cannot influence itself through a citation cycle, the SimSPC algorithm is proposed to weight citation arcs by counting simple search paths. A set of algorithms are further proposed for main path exploration and extraction directly from cyclic networks based on a novel data structure main path tree. The experiments on two cyclic citation networks demonstrate the usefulness of the alternative solution. In the meanwhile, experiments show that publications in strongly connected components may sit on the turning points of main path networks, which signifies the necessity of a systematic way of dealing with citation cycles.

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Abstract

This study proposes and explores a natural language processing- (NLP) based strategy to address out-ofdictionary and vocabulary mismatch problems in query translation based English-Chinese Cross-Language Information Retrieval (EC-CLIR). The strategy, named the LKB approach, is to construct a lexical knowledge base (LKB) and to use it for query translation. In this article, the author describes the LKB construction process, which customizes available translation resources based an the document collection of the EC-CLIR system. The evaluation shows that the LKB approach is very promising. It consistently increased the percentage of correct translations and decreased the percentage of missing translations in addition to effectively detecting the vocabulary gap between the document collection and the translation resource of the system. The comparative analysis of the top EC-CLIR results using the LKB and two other translation resources demonstrates that the LKB approach has produced significant improvement in EC-CLIR performance compared to performance using the original translation resource without customization. It has also achieved the same level of performance as a sophisticated machine translation system. The study concludes that the LKB approach has the potential to be an empirical model for developing real-world CLIR systems. Linguistic knowledge and NLP techniques, if appropriately used, can improve the effectiveness of English-Chinese crosslanguage information retrieval.