Search (158 results, page 1 of 8)

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Source

https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

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Abstract

There is a mutual resemblance between the behavior of users of the Stack Exchange and the dynamics of the citations accumulation process in the scientific community, which enabled us to tackle the outwardly intractable problem of assessing the impact of introducing "negative" citations. Although the most frequent reason to cite an article is to highlight the connection between the 2 publications, researchers sometimes mention an earlier work to cast a negative light. While computing citation-based scores, for instance, the h-index, information about the reason why an article was mentioned is neglected. Therefore, it can be questioned whether these indices describe scientific achievements accurately. In this article we shed insight into the problem of "negative" citations, analyzing data from Stack Exchange and, to draw more universal conclusions, we derive an approximation of citations scores. Here we show that the quantified influence of introducing negative citations is of lesser importance and that they could be used as an indicator of where the attention of the scientific community is allocated.

Date

6. 1.2020 18:10:22

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Abstract

The review attempts to compare different points of view on the essence of the systems approach, describe the terminological confusion around it and analyse the numerous definitions of system. It is shown that the vagueness and ambiguity of the concept of the systems approach is manifested in the use of a number of terms which are similar in meaning and close in sound to it. It is proposed to divide the existing definitions of system into descriptive and formal ones. The concepts included in the descriptive definitions, as well as the numerous synonymous terms denoting them, are divided into five conceptual-terminological groups that differ in their content and logical meaning. The meanings of such concepts as minimal constituent parts, emergence, environment, boundaries, purpose, functions of system and systems hierarchy are revealed. Some uses of the concept in knowledge organization are mentioned. The problem of systems classification is touched upon. Separate sections are devoted to the highlights of the history of the systems approach, its criticism and the significance. Particular attention is paid to criticism of the mathematization of the systems approach. Possible reasons for the decline in interest in the systems approach are identified. It is concluded that the systems approach helps to find new ways to solve scientific and practical problems.

Date

20.11.2023 13:36:29

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Abstract

Purpose The purpose of this paper is to identify key countries and their focal research fields on the information divide. Design/methodology/approach Literature was retrieved to identify key countries and their primary focus. The literature research method was adopted to identify aspects of the primary focus in each key country. Findings The key countries with literature on the information divide are the USA, China, the UK and India. The problem of health is prominent in the USA, and solutions include providing information, distinguishing users' profiles and improving eHealth literacy. Economic and political factors led to the urban-rural information divide in China, and policy is the most powerful solution. Under the influence of humanism, research on the information divide in the UK focuses on all age groups, and solutions differ according to age. Deep-rooted patriarchal concepts and traditional marriage customs make the gender information divide prominent in India, and increasing women's information consciousness is a feasible way to reduce this divide. Originality/value This paper is an extensive review study on the information divide, which clarifies the key countries and their focal fields in research on this topic. More important, the paper innovatively analyzes and summarizes existing literature from a country perspective.

Date

13. 2.2020 18:22:13

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Abstract

In this article, we present a method to automatically build large labeled datasets for the author ambiguity problem in the academic world by leveraging the authoritative academic resources, ORCID and DOI. Using the method, we built LAGOS-AND, two large, gold-standard sub-datasets for author name disambiguation (AND), of which LAGOS-AND-BLOCK is created for clustering-based AND research and LAGOS-AND-PAIRWISE is created for classification-based AND research. Our LAGOS-AND datasets are substantially different from the existing ones. The initial versions of the datasets (v1.0, released in February 2021) include 7.5 M citations authored by 798 K unique authors (LAGOS-AND-BLOCK) and close to 1 M instances (LAGOS-AND-PAIRWISE). And both datasets show close similarities to the whole Microsoft Academic Graph (MAG) across validations of six facets. In building the datasets, we reveal the variation degrees of last names in three literature databases, PubMed, MAG, and Semantic Scholar, by comparing author names hosted to the authors' official last names shown on the ORCID pages. Furthermore, we evaluate several baseline disambiguation methods as well as the MAG's author IDs system on our datasets, and the evaluation helps identify several interesting findings. We hope the datasets and findings will bring new insights for future studies. The code and datasets are publicly available.

Date

22. 1.2023 18:40:36

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Date

21. 3.2023 19:22:29

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Abstract

Purpose Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions and information dissemination in all major forms, including on social media. This article investigates important issues reflected on Twitter in the early stages of the public reaction to COVID-19. Design/methodology/approach A thematic analysis of the most retweeted English-language tweets mentioning COVID-19 during March 10-29, 2020. Findings The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news. Research limitations/implications Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers and helped each other cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed. Practical implications Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues. Originality/value This is the first qualitative analysis of general COVID-19-related retweeting.

Date

20. 1.2015 18:30:22

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Abstract

Interdisciplinarity implies the previous existence of disciplinary fields and not their dissolution. As a general objective, we propose to establish an initial approach to the emphasis given to interdisciplinarity in the teaching of KO, through the teaching staff responsible for postgraduate courses focused on -or related to the KO, in Ibero-American universities. For conducting the research, the framework and distribution of a survey addressed to teachers is proposed, based on four lines of action: 1. The way teachers manage the concept of interdisciplinarity. 2. The place that teachers give to interdisciplinarity in KO. 3. Assessment of interdisciplinary content that teachers incorporate into their postgraduate courses. 4. Set of teaching strategies and resources used by teachers to include interdisciplinarity in the teaching of KO. The study analyzed 22 responses. Preliminary results show that KO teachers recognize the influence of other disciplines in concepts, theories, methods, and applications, but no consensus has been reached regarding which disciplines and authors are the ones who build interdisciplinary bridges. Among other conclusions, the study strongly suggests that environmental and social tensions are reflected in subject representation, especially in the construction of friendly knowl­edge organization systems with interdisciplinary visions, and in the expressions through which information is sought.

Date

20.11.2023 17:29:13

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Abstract

Siri, Google Search, online marketing and your child's homework will never be the same. Then there's the misinformation problem.

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Abstract

Author name disambiguation (AND) is a challenging problem due to several issues such as missing key identifiers, same name corresponding to multiple authors, along with inconsistent representation. Several techniques have been proposed but maintaining consistent accuracy levels over all data sets is still a major challenge. We identify two major issues associated with the AND problem. First, the namesake problem in which two or more authors with the same name publishes in a similar domain. Second, the diverse topic problem in which one author publishes in diverse topical domains with a different set of coauthors. In this work, we initially propose a method named ATGEP for AND that addresses the namesake issue. We evaluate the performance of ATGEP using various ambiguous name references collected from the Arnetminer Citation (AC) and Web of Science (WoS) data set. We empirically show that the two aforementioned problems are crucial to address the AND problem that are difficult to handle using state-of-the-art techniques. To handle the diverse topic issue, we extend ATGEP to a new variant named ATGEP-web that considers external web information of the authors. Experiments show that with enough information available from external web sources ATGEP-web can significantly improve the results further compared with ATGEP.

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Abstract

This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users' privacy protection, i.e., it is required to comprehensively improve the security of users' preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.

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Abstract

This article addresses the problem of detecting crisis-related messages on social media, in order to improve the situational awareness of emergency services. Previous work focused on developing machine-learning classifiers restricted to specific disasters, such as storms or wildfires. We investigate for the first time methods to detect such messages where the type of the crisis is not known in advance, that is, the data are highly heterogeneous. Data heterogeneity causes significant difficulties for learning algorithms to generalize and accurately label incoming data. Our main contributions are as follows. First, we evaluate the extent of this problem in the context of disaster management, finding that the performance of traditional learners drops by up to 40% when trained and tested on heterogeneous data vis-á-vis homogeneous data. Then, in order to overcome data heterogeneity, we propose a new ensemble learning method, and found this to perform on a par with the Gradient Boosting and AdaBoost ensemble learners. The methods are studied on a benchmark data set comprising 26 disaster events and four classification problems: detection of relevant messages, informative messages, eyewitness reports, and topical classification of messages. Finally, in a case study, we evaluate the proposed methods on a real-world data set to assess its practical value.

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Abstract

Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field, and solutions to them are many years off. However, new technological developments allow the use of knowledge organization and machine learning in multimedia search systems and services. Specifically, we argue that, the improvement of detection of some classes of lowlevel features in images music and video can be used in conjunction with knowledge organization to tag or label multimedia content for better retrieval performance. We provide an overview of the use of knowledge organization schemes in machine learning and make recommendations to information professionals on the use of this technology with knowledge organization techniques to solve multimedia IR problems. We introduce a five-step process model that extracts features from multimedia objects (Step 1) from both knowledge organization (Step 1a) and machine learning (Step 1b), merging them together (Step 2) to create an index of those multimedia objects (Step 3). We also overview further steps in creating an application to utilize the multimedia objects (Step 4) and maintaining and updating the database of features on those objects (Step 5).

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Abstract

Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word-level indicative representation and message-level indicative representation hierarchically. In both of the modules, we apply character-level representations, which enables the model to explore morphological features and deal with the out-of-vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real-life data sets indicate that our model outperforms state-of-the-art models from previous studies.

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Abstract

This article presents approaches used to solve the problem of selecting AI technologies and tools to obtain the creativity fostering functionalities of an innovative knowledge platform. The aforementioned selection problem has been lagging behind other software-specific aspects of online knowledge platform and learning platform development so far. We linked technological recommendations from group decision support exercises to the platform design aims and constraints using an expert Delphi survey and multicriteria analysis methods. The links between expected advantages of using selected AI building tools, AI-related system functionalities, and their ongoing relevance until 2030 were assessed and used to optimize the learning scenarios and in planning the future development of the platform. The selected technologies allowed the platform management to implement the desired functionalities, thus harnessing the potential of open innovation platforms more effectively and delivering a model for the development of a relevant class of advanced open-access knowledge provision systems. Additionally, our approach is an essential part of digital sustainability and AI-alignment strategy for the aforementioned class of systems. The knowledge platform, which serves as a case study for our methodology has been developed within an EU Horizon 2020 research project.

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Date

18. 8.2022 19:22:57

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Abstract

This article considers the problem of preserving research software within the wider realm of digital curation, academic research libraries, and the scholarly record. We conducted a pilot study to understand the ecosystem in which research software participates, and to identify significant characteristics that have high potential to support future scholarly practices. A set of topical curation dimensions were derived from the extant literature and applied to select cases of institutionally significant research software. This approach yields our main contribution, a curation model and decision framework for preserving research software as a scholarly object. The results of our study highlight the unique characteristics and challenges at play in building curation services in academic research libraries.

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Abstract

Contrary to optimistic visions of a free internet for all, the problem of the 'digital divide' has persisted for close to twenty-five years. Jan van Dijk considers the state of digital inequality and what we can do to tackle it

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Abstract

This article presents a rare insight into the migration of municipality record-keeping databases. The migration of a database for preservation purposes poses several challenges. In particular, our findings show that relevant issues are file-format heterogeneity, collection volume, time and database structure evolution, and deviation from the governing standard. This article presents and discusses how such issues interfere with an organization's ability to undertake a migration, for preservation purposes, of records from a relational database. The case study at hand concerns six Norwegian municipality record-keeping databases covering a period from 1999 to 2012. The findings are presented with a discussion on how these issues manifest themselves as a problem for long-term preservation. The results discussed here may help an organization and Information Systems (IS) manager to establish a best practice when undertaking a migration project and enable them to avoid some of the pitfalls that were discovered during this project.

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Abstract

Novel ideas often experience resistance from incumbent forces. While evidence of the bias against novelty has been widely identified in science, there is still a lack of large-scale quantitative work to study this problem occurring in the prepublication process of manuscripts. This paper examines the association between manuscript novelty and handling time of publication based on 778,345 articles in 1,159 journals indexed by PubMed. Measuring the novelty as the extent to which manuscripts disrupt existing knowledge, we found systematic evidence that higher novelty is associated with longer handling time. Matching and fixed-effect models were adopted to confirm the statistical significance of this pattern. Moreover, submissions from prestigious authors and institutions have the advantage of shorter handling time, but this advantage is diminishing as manuscript novelty increases. In addition, we found longer handling time is negatively related to the impact of manuscripts, while the relationships between novelty and 3- and 5-year citations are U-shape. This study expands the existing knowledge of the novelty bias by examining its existence in the prepublication process of manuscripts.