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625 results on '"Foo, Schubert"'

6. Survey on Inadequate and Omitted Citations in Manuscripts: A Precursory Study in Identification of Tasks for a Literature Review and Manuscript Writing Assistive System

Author

Raamkumar, Aravind Sesagiri, Foo, Schubert, and Pang, Natalie

Abstract

Introduction: This paper looks at the issue of inadequate and omitted citations in manuscripts by collecting the experiential opinions of researchers from the dual perspectives of manuscript reviewers and authors. Method: An online survey was conducted with participation from 207 respondents who had experience of reviewing and authoring research papers. Analysis: The collected data were analysed quantitatively. Descriptive and bivariate analyses were performed. Results: Reviewer and author groups opined that manuscript authors fail to cite seminal and topically-similar papers, while the reviewer group indicated that authors include too few papers and cite irrelevant papers. The lack of experience was perceived as a major reason for inadequate and omitted citations, followed by lack of overall research experience and working in interdisciplinary research projects. Authors needed external assistance in finding papers for a literature review. Google Scholar was the most used system among the list of information sources. Conclusions: The findings may benefit subsequent studies conducted to solve the issue of inadequate and omitted citations through process improvements and technological interventions. The findings helped in identifying three tasks for a literature review and manuscript writing assistive system. The usage preferences on information sources helped in shortlisting Google Scholar's user-interface as a basis for the user-interface design for the assistive system. [Paper presented at the Information Seeking in Context (ISIC): The Information Behaviour Conference, Part 1 (11th, Zadar, Croatia, September 20-23, 2016).]

Published
2016

7. A Framework for Scientific Paper Retrieval and Recommender Systems

Author

Raamkumar, Aravind Sesagiri, Foo, Schubert, and Pang, Natalie

Subjects
Computer Science - Information Retrieval, H.3.0
Abstract

Information retrieval (IR) and recommender systems (RS) have been employed for addressing search tasks executed during literature review and the overall scholarly communication lifecycle. Majority of the studies have concentrated on algorithm design for improving the accuracy and usefulness of these systems. Contextual elements related to the scholarly tasks have been largely ignored. In this paper, we propose a framework called the Scientific Paper Recommender and Retrieval Framework (SPRRF) that combines aspects of user role modeling and user-interface features with IR/RS components. The framework is based on eight emergent themes identified from participants feedback in a user evaluation study conducted with a prototype assistive system. 119 researchers participated in the study for evaluating the prototype system that provides recommendations for two literature review and one manuscript writing tasks. This holistic framework is meant to guide future studies in this area., Comment: Technical Report, 2 tables, 1 figure

Published
2016

27. Upper Tag Ontology (UTO) For Integrating Social Tagging Data

Author

Ding, Ying, Jacob, Elin K., Fried, Michael, Toma, Ioan, Yan, Erjia, and Foo, Schubert

Subjects
Computer Science - Information Retrieval, Computer Science - Social and Information Networks
Abstract

Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies capitalize on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in harvesting, modeling, integrating, searching and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr and YouTube)., Comment: 31 pages, 7 figures

Published
2010

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