Innovation as a Nonlinear Process, the Scientometric Perspective, and the Specification of an "Innovation Opportunities Explorer"

The process of innovation follows non-linear patterns across the domains of science, technology, and the economy. Novel bibliometric mapping techniques can be used to investigate and represent distinctive, but complementary perspectives on the innovation process (e.g., "demand" and "supply") as well as the interactions among these perspectives. The perspectives can be represented as "continents" of data related to varying extents over time. For example, the different branches of Medical Subject Headings (MeSH) in the Medline database provide sources of such perspectives (e.g., "Diseases" versus "Drugs and Chemicals"). The multiple-perspective approach enables us to reconstruct facets of the dynamics of innovation, in terms of selection mechanisms shaping localizable trajectories and/or resulting in more globalized regimes. By expanding the data with patents and scholarly publications, we demonstrate the use of this multi-perspective approach in the case of RNA Interference (RNAi). The possibility to develop an "Innovation Opportunities Explorer" is specified.Comment: Technology Analysis and Strategic Management (forthcoming in 2013

Social Network Technologies for Semantic Linking of Information Objects in Scientific Digital Library

Abstract. In the last decade, scientific digital libraries were traditionally used for publishing research results and for enabling wide open access to them. Functional capabilities of digital libraries can be extended by offering users the opportunity of linking information objects of the library and providing for created linkages explicitly defined semantics based on a given ontology. Such an activity of users, which is peculiar to social networks, motivated by different reasons, and carried out on their own initiative, results in the dynamic semantic structure of the digital library content. In the environment of such a kind of a social network, certain new forms of scientific activities become possible and data sources can be created that provide more information for scientometric researches as compared to presently available ones. In this paper, we propose an approach for creating such networks and discuss results of its implementation in the Socionet environment; the Socionet is a large-scale online information space that covers information resources of a number of scientific, educational, etc. organizations. This work was supported by the Russian Foundation for Humanities, project no. 14-02-12010-v

An integrative literature review of social entrepreneurship research: mapping the literature and future research directions

This article maps existing research from 5,874 scholarly publications on social entrepreneurship (SE) utilizing scientometrics. The mapping indicates a taxonomy of five clusters: (a) the nature of SE, (b) policy implications and employment in relation to SE, (c) SE in communities and health, (d) SE personality traits, and (e) SE education. We complement the scientometric analysis with a systematic literature review of publications on SE in the Financial Times 50 list (FT50) and Business & Society and propose a multistage, multilevel framework that highlights the clusters of existing research on SE based on their stage and level of analysis. This review study also helps outline a set of future research directions, including studies examining (a) the process stage at the micro-level and macro-level, (b) linkages across levels and stages, (c) linkages across stages over time or longitudinal studies, (d) SE in resource-constrained environments, (e) technological advancement and its impact on SE, (f) the types of social enterprises and their outcomes, and (g) various emerging topics in SE

Recent Trends in Digital Library Publications: A Scientometric Analysis

The study seeks to illustrate the most current trends in digital library research via the use of scientometrics. The study of scientific networks is important in many scientific domains. A social network with many nodes and connections serves as the foundation for scientific network research. Nodes include authors, publications, and journals, while linkages include citations, cocitations, and coauthorship. Data was collected from the Scopus abstracting and citation database for the period of ten years from 2012 to 2021. The most relevant 1957 documents were chosen from the collection, and selected documents were analyzed using Biblioshny and VOSviewer. The research showed that digital library productivity is rising annually, the United States of America dominates the production of scholarly production on digital libraries, and research is increasingly focused on digital resource and digital collection development. However, artificial intelligence, deep learning, machine learning, big data, and other related areas of study have emerged as the most recent research trends in digital library research. The outcomes of this study will aid digital library research by providing up-to-date and reliable research information

Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining

Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. Text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical Text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches proven to be successfully reducing the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting it with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 still remains as a core task in text mining. Currently the ABC principle based two approaches namely open discovery and closed discovery are mostly explored in LBD process. This review starts with general introduction about text mining followed by biomedical text mining and introduces various literature resources such as MEDLINE, UMLS, MESH, and SemMedDB. This is followed by brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in LBD process. This review also discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks based LBD models and its future aspects. Finally, reviews the key biomedical discoveries generated through LBD approaches in biomedicine and conclude with the current limitations and future directions of LBD.Comment: 43 Pages, 5 Figures, 4 Table