Automatic Metadata Generation using Associative Networks

In spite of its tremendous value, metadata is generally sparse and incomplete, thereby hampering the effectiveness of digital information services. Many of the existing mechanisms for the automated creation of metadata rely primarily on content analysis which can be costly and inefficient. The automatic metadata generation system proposed in this article leverages resource relationships generated from existing metadata as a medium for propagation from metadata-rich to metadata-poor resources. Because of its independence from content analysis, it can be applied to a wide variety of resource media types and is shown to be computationally inexpensive. The proposed method operates through two distinct phases. Occurrence and co-occurrence algorithms first generate an associative network of repository resources leveraging existing repository metadata. Second, using the associative network as a substrate, metadata associated with metadata-rich resources is propagated to metadata-poor resources by means of a discrete-form spreading activation algorithm. This article discusses the general framework for building associative networks, an algorithm for disseminating metadata through such networks, and the results of an experiment and validation of the proposed method using a standard bibliographic dataset

E-learning in mixed reality landscape: emerging issues and key trends in scientific research

E-Learning aims to apply information and communications technology to enhance and support the learning process and is a popular mode of delivering educational materials in universities throughout the world. Recently, due to the advancements of Augmented Reality (AR) and Virtual reality (VR), the E-learning process has great challenges and opportunities ahead. This study intends to explore the current trends in the field of AR/VR applied to distance education research. Using bibliographic data extracted from the Scopus® database and social network analysis techniques were able to analyse author keyword toward the identification of major trends. For the analysis, we collected keywords from research papers published in international journals related to E-learning, AR and VR, between 2006 and 2017, and constructed a co-word network, and then conducted the keywords network analysis. Retrieving the "E-learning" ego-network we could find some clusters that define major trends like virtual environments or evaluations process. The study reveals that E-learning process fits better in AR than VR research. The findings obtained in this study may be useful in the exploration of potential research areas in the field of distance education.info:eu-repo/semantics/acceptedVersio

Literature review on the ‘Smart Factory’ concept using bibliometric tools

The objective of this paper is to depict a landscape of the scientific literature on the concept of the ‘Smart Factory’, which in recent years is gaining more and more attention from academics and practitioners because of significant innovations in the production systems within the manufacturing sector. To achieve this objective, a dynamic methodology called "Systematic Literature Network Analysis (SLNA)" has been applied. This methodology combines the Systematic Literature Review approach with the analysis of bibliographic networks. The adopted methodology allows complementing traditional content-based literature reviews by extracting quantitative information from bibliographic networks to detect emerging topics, and by revealing the dynamic evolution of the scientific production of a discipline. This dynamic analysis allowed highlighting research directions and critical areas for the development of the "Smart Factory". At the same time, it offers insights on the fields on which companies, associations, politicians and technology providers need to focus in order to allow a real transition towards the implementation of large-scale Smart Factory

Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis

The aim of the present research is to provide a new systematic methodology to explore potential R&D collaboration partners using patent information. The potential R&D collaboration partners are visualized as a patent assignee level-map based on technological similarity between patents by using network analysis. The proposed framework utilises two analytic methods to measure technological similarity. The first method, bibliographic coupling analysis, measures technological similarity based on the citation relationship using patent bibliographic information. Second, latent semantic analysis is utilized based on semantic similarity using patent textual information. The fuel cell membrane electrode assembly (MEA) technology field is selected and applied to illustrate the proposed methodology. The proposed approach allows firms, universities, research institutes, governments to identify potential R&D collaborators as a systematic decision-making support tool.This is an Accepted Manuscript of an article published by Taylor & Francis in Technology Analysis and Strategic Management on the 22nd of October 2014, available online: http://wwww.tandfonline.com/10.1080/09537325.2014.971004. This version will be under embargo until the 22nd of April 2016

Detección de comunidades científicas en los tribunales de tesis en el área de marketing

En esta contribución se presenta un análisis de los tribunales de defensa de las tesis doctorales españolas leídas en el área de marketing, basado en redes bibliográficas y detección de comunidades. Los datos fueron descargados de la base de datos TESEO, seguido de un proceso de filtrado en el que se seleccionaron sólo aquellas tesis en las que la palabra “marketing” aparecía en el título, en el nombre del programa de doctorado o en los descriptores. Mediante técnicas bibliométricas, se construyó una red basada en la relación de co-ocurrencia de los miembros del tribunal de defensa. Finalmente, mediante algoritmos de detección de comunidades, se detectan los distintos grupos de miembros que suelen pertenecer de manera conjunta a los tribunales de defensa.In this contribution, an analysis of the marketing theses defended in Spain is carried out based on bibliographic networks and communities detection. Data was retrieved from TESEO database. Then, a filtering process was applied in order to get only theses data in which doctoral programs, thesis title or keywords match with terms related with Marketing. Then, we build a network with theses defense committee members from the data extracted and we apply fundamentals of social network analysis and community detection techniques to analyze data and uncover novel characteristics from network structure

On Fractional Approach to Analysis of Linked Networks

In this paper, we present the outer product decomposition of a product of compatible linked networks. It provides a foundation for the fractional approach in network analysis. We discuss the standard and Newman's normalization of networks. We propose some alternatives for fractional bibliographic coupling measures

Co-authorship network and the correlation with academic performance

This paper aims to study the internal structure of the co-authorship network and the relationship between the network and the authors’ academic performance in the network. In order to conduct this research, bibliographic data of 166 authors from three top higher education institutions of Shanghai was collected and the method of social network analysis (SNA) was performed to analyze the data. In the link analysis, the centrality, egocentric network efficiency, authorities, and hubs were analyzed. In the graph cluster analysis, this paper employs clustering algorithms based on betweenness. Lastly, the Spearman correlation test was performed to analyze the relationship between academic performance and SNA metrics. This paper found that and betweenness centrality, eigenvector centrality, authority and hub position, and efficiency were significant to g-index. The research provided a glimpse of the co-authorship network's internal structure in China. Additionally, the SNA method of identifying productive scholars can also be applied to other areas, such as the network of equipment in the Industry 5.0 to help companies identify the strong and weak links in the producing process

The Extraction of Community Structures from Publication Networks to Support Ethnographic Observations of Field Differences in Scientific Communication

The scientific community of researchers in a research specialty is an important unit of analysis for understanding the field specific shaping of scientific communication practices. These scientific communities are, however, a challenging unit of analysis to capture and compare because they overlap, have fuzzy boundaries, and evolve over time. We describe a network analytic approach that reveals the complexities of these communities through examination of their publication networks in combination with insights from ethnographic field studies. We suggest that the structures revealed indicate overlapping sub- communities within a research specialty and we provide evidence that they differ in disciplinary orientation and research practices. By mapping the community structures of scientific fields we aim to increase confidence about the domain of validity of ethnographic observations as well as of collaborative patterns extracted from publication networks thereby enabling the systematic study of field differences. The network analytic methods presented include methods to optimize the delineation of a bibliographic data set in order to adequately represent a research specialty, and methods to extract community structures from this data. We demonstrate the application of these methods in a case study of two research specialties in the physical and chemical sciences.Comment: Accepted for publication in JASIS