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Open access

Alireza Abbasi and Ali Jaafari

Abstract

In this study, we investigated the quantity and impact of worldwide research production in the field of “project management” over the past 38 years. We performed a bibliometric analysis using the Scopus database between 1980 and 2017 to develop an understanding of the evolution of research on “project management.” Using the knowledge of a domain expert in the field of “project management,” we first compiled a set of reliable keywords, which represented the field. Second, we developed a data extraction strategy for searching the phrase “project management” in the title or keyword or abstract of publications by limiting our sources to journals in English. We observed the evolution of this field by analyzing not only the quantity of publications but also their impact (citations) per year and compared their growth trend in four periods. The results of our analysis confirmed that not only the research themes or topics but also the active parties involved in project management research have experienced phonemic changes over time.

Open access

Tolga Yuret

Abstract

Citation performance of a publication depends heavily on its academic field. Some words in keywords, titles, and abstracts of publications may be indicative of their academic field. Therefore, analysis of differences in citation performance of these words helps us understand inter-field differences in citation performance. In this article, we analyzed citation performance of publications that contain certain words in their keywords, titles, and abstracts in Web of Science from 2010 to 2012. We found that some words do not have a consistent performance. For instance, publications that use a certain word in their keywords have a different average performance compared to publications that use the same word in their titles. Next, we investigated keywords, titles, and abstracts separately. We laid out the words that have the lowest and highest average citations. Words that contain animal names, country names, and mathematical concepts are among the worst performers. Words that contain terminology specific to a scientific field and have relatively lower frequency are among the best performers.

Open access

Xia Cuijuan, Liu Wei and Zhang Lei

Abstract

Linked data is becoming a mature technology as a lightweight realization of the Semantic Web, as well as a way of facilitating knowledge reorganization and discovery. As a use case and start point, based on linked data technology, a genealogy knowledge service platform was implemented by the Shanghai Library for providing knowledge discovery and open data services. This article explains the design and development of the Genealogy Knowledge Service Platform, describes the method and process of the implementation, and introduces four examples of how the platform helps users to discover questions, raise questions, and solve questions for their research, to explain how Linked Data can be used in Digital Humanities.

Open access

Christina Lioma, Birger Larsen and Peter Ingwersen

Abstract

When submitting queries to information retrieval (IR) systems, users often have the option of specifying which, if any, of the query terms are heavily dependent on each other and should be treated as a fixed phrase, for instance by placing them between quotes.In addition to such cases where users specify term dependence, automatic ways also exist for IR systems to detect dependent terms in queries. Most IR systems use both user and algorithmic approaches. It is not however clear whether and to what extent user-defined term dependence agrees with algorithmic estimates of term dependence, nor which of the two may fetch higher performance gains. Simply put, is it better to trust users or the system to detect term dependence in queries? To answer this question, we experiment with 101 crowdsourced search engine users and 334 queries (52 train and 282 test TREC queries) and we record 10 assessments per query. We find that (i) user assessments of term dependence differ significantly from algorithmic assessments of term dependence (their overlap is approximately 30%); (ii) there is little agreement among users about term dependence in queries, and this disagreement increases as queries become longer; (iii) the potential retrieval gain that can be fetched by treating term dependence (both user- and system-defined) over a bag of words baseline is reserved to a small subset (approximately 8%) of the queries, and is much higher for low-depth than deep precision measures. Points (ii) and (iii) constitute novel insights into term dependence.

Open access

Neil R. Smalheiser and Aaron M. Cohen

Abstract

Many investigators have carried out text mining of the biomedical literature for a variety of purposes, ranging from the assignment of indexing terms to the disambiguation of author names. A common approach is to define positive and negative training examples, extract features from article metadata, and use machine learning algorithms. At present, each research group tackles each problem from scratch, in isolation of other projects, which causes redundancy and a great waste of effort. Here, we propose and describe the design of a generic platform for biomedical text mining, which can serve as a shared resource for machine learning projects and as a public repository for their outputs. We initially focus on a specific goal, namely, classifying articles according to publication type and emphasize how feature sets can be made more powerful and robust through the use of multiple, heterogeneous similarity measures as input to machine learning models. We then discuss how the generic platform can be extended to include a wide variety of other machine learning-based goals and projects and can be used as a public platform for disseminating the results of natural language processing (NLP) tools to end-users as well.

Open access

Yiming Zhao, Baitong Chen, Jin Zhang, Ying Ding, Jin Mao and Lihong Zhou

Abstract

This study investigates the evolution of diabetics’ concerns based on the analysis of terms in the Diabetes category logs on the Yahoo! Answers website. Two sets of question-and-answer (Q&A) log data were collected: one from December 2, 2005 to December 1, 2006; the other from April 1, 2013 to March 31, 2014. Network analysis and a t-test were performed to analyze the differences in diabetics’ concerns between these two data sets. Community detection and topic evolution were used to reveal detailed changes in diabetics’ concerns in the examined period. Increases in average node degree and graph density imply that the vocabulary size that diabetics use to post questions decreases while the scope of questions has become more focused. The networks of key terms in the Q&A log data of 2005–2006 and 2013–2014 are significantly different according to the t-test analysis of the degree centrality and betweenness centrality. Specifically, there is a shift in diabetics’ focus in that they have become more concerned about daily life and other nonmedical issues, including diet, food, and nutrients. The recent changes and the evolution paths of diabetics’ concerns were visualized using an alluvial diagram. The food- and diet-related terms have become prominent, as deduced from the visualization results.

Open access

Fred Y. Ye and Lutz Bornmann

Abstract

The second-order h-type indicators are suggested to identify top units in scientometrics. Basically, the re-ranking of h-type series leads to the second-order h-type indicator. The second-order h-type indicators provide an interesting and natural method to identify top units, yielding fixed h-top. Differentiating from the series of artificially defined highly cited percentile classes, the h-top contributes a natural definite top in the series of highly cited classes. When studying theoretically, the second-order h-index concerns 3% of the h-top whereas the first-order h-index refers to 10% of the h-core. The ratio of the first- and second-order h-index, hT/h, is 30%. When studying empirically, the ratio of the first- and second-order h-index, hT/h, is <30%. The approach of calculating second-order h-type indicators is exemplified based on journals in two fields.

Open access

Eric Zheng, Yong Tan, Paulo Goes, Ramnath Chellappa, D.J. Wu, Michael Shaw, Olivia Sheng and Alok Gupta

Open access

Ahmed AlKalbani, Hepu Deng, Booi Kam and Xiaojuan Zhang

Abstract

The increasing recognition of the importance of information security has created institutional pressures on organizations to comply with information security standards and policies for protecting their information. How such pressures influence information security compliance in organisations, however, is unclear. This paper presents an empirical study to investigate the impact of institutional pressures on information security compliance in organizations. With the use of structural equation modelling for analysing the data collected through an online survey, the study shows that coercive pressures, normative pressures, and mimetic pressures positively influence information security compliance in organizations. It reveals that the benefits of information security compliance motivate management to strengthen their commitments at information security compliance. Furthermore, the study finds out that social pressures do not have a significant impact on management commitments towards information security compliance. Theoretically this study contributes to the information security research by better understanding how institutional pressures can be used for enhancing information security compliance in organizations. Practically this study informs information security policy makers of the major institutional drivers for information security compliance.

Open access

Dong Wang, Lei Zou and Dongyan Zhao

Abstract

The Simple Protocol and RDF Query Language (SPARQL) query language allows users to issue a structural query over a resource description framework (RDF) graph. However, the lack of a spatiotemporal query language limits the usage of RDF data in spatiotemporal-oriented applications. As the spatiotemporal information continuously increases in RDF data, it is necessary to design an effective and efficient spatiotemporal RDF data management system. In this paper, we formally define the spatiotemporal information-integrated RDF data, introduce a spatiotemporal query language that extends the SPARQL language with spatiotemporal assertions to query spatiotemporal information-integrated RDF data, and design a novel index and the corresponding query algorithm. The experimental results on a large, real RDF graph integrating spatial and temporal information (> 180 million triples) confirm the superiority of our approach. In contrast to its competitors, gst-store outperforms by more than 20%-30% in most cases.