Computational Generalization in Taxonomies Applied to: (1) Analyze Tendencies of Research and (2) Extend User Audiences

D.F. and B.M. acknowledge continuing support by the Academic Fund Program at the NRU HSE (grant-19-04-019 in 2018?2019) and by the DECAN Lab NRU HSE, in the framework of a subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the Russian Academic Excellence Project ?5-100?. S.N. acknowledges the support by FCT/MCTES, NOVA LINCS (UID/CEC/04516/2019).We define a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a domain taxonomy. This generalization lifts the set to its “head subject” node in the higher ranks of the taxonomy tree. The head subject is supposed to “tightly” cover the query set, possibly bringing in some errors referred to as “gaps” and “offshoots”. Our method, ParGenFS, globally minimizes a penalty function combining the numbers of head subjects and gaps and offshoots, differently weighted. Two applications are considered: (1) analysis of tendencies of research in Data Science; (2) audience extending for programmatic targeted advertising online. The former involves a taxonomy of Data Science derived from the celebrated ACM Computing Classification System 2012. Based on a collection of research papers published by Springer 1998–2017, and applying in-house methods for text analysis and fuzzy clustering, we derive fuzzy clusters of leaf topics in learning, retrieval and clustering. The head subjects of these clusters inform us of some general tendencies of the research. The latter involves publicly available IAB Tech Lab Content Taxonomy. Each of about 25 mln users is assigned with a fuzzy profile within this taxonomy, which is generalized offline using ParGenFS. Our experiments show that these head subjects effectively extend the size of targeted audiences at least twice without loosing quality.authorsversionpublishe

Using Taxonomy Tree to Generalize a Fuzzy Thematic Cluster

D.F. and B.M. acknowledge continuing support by the Academic Fund Program at the National Research University Higher School of Economics (grant 19-04-019 in 2018-2019) and by the International Decision Choice and Analysis Laboratory (DECAN) NRU HSE, in the framework of a subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the the Russian Academic Excellence Project “5-100”. S.N. acknowledges the support by FCT/MCTES, NOVA LINCS (UID/CEC/04516/2019).This paper presents an algorithm, ParGenFS, for generalizing, or 'lifting', a fuzzy set of topics to higher ranks of a hierarchical taxonomy of a research domain. The algorithm ParGenFS finds a globally optimal generalization of the topic set to minimize a penalty function, by balancing the number of introduced 'head subjects' and related errors, the 'gaps' and 'offshoots', differently weighted. This leads to a generalization of the topic set in the taxonomy. The usefulness of the method is illustrated on a set of 17685 abstracts of research papers on Data Science published in Springer journals for the past 20 years. We extracted a taxonomy of Data Science from the international Association for Computing Machinery Computing Classification System 2012 (ACM-CCS). We find fuzzy clusters of leaf topics over the text collection, lift them in the taxonomy, and interpret found head subjects to comment on the tendencies of current research.authorsversionpublishe

Computational intelligence for measuring macro-knowledge competitiveness

The aim of this research is to investigate the utilisation of Computational Intelligence methods for constructing Synthetic Composite Indicators (SCI). In particular for delivering a Unified Macro-Knowledge Competitiveness Indicator (UKCI) to enable consistent and transparent assessments and forecasting of the progress and competitiveness of Knowledge Based Economy (KBE). SCI are assessment tools usually constructed to evaluate and contrast entities performance by aggregating intangible measures in many areas such as economy, education, technology and innovation. SCI key value is inhibited in its capacity to aggregate complex and multi-dimensional variables into a single meaningful value. As a result, SCIs have been considered as one of the most important tools for macro-level and strategic decision making. Considering the shortcomings of the existing SCI, this study is proposing an alternative approach to develop Intelligent Synthetic Composite Indicators (iSCI). The suggested approach utilizes Fuzzy Proximity Knowledge Mining technique to build the qualitative taxonomy initially, and Fuzzy c-mean is employed to form the new composite indicators

A comparative framework for broad-scale plot-based vegetation classification

Aims: Classification of vegetation is an essential tool to describe, understand, predict and manage biodiversity. Given the multiplicity of approaches to classify vegetation, it is important to develop international consensus around a set of general guidelines and purpose-specific standard protocols. Before these goals can be achieved, however, it is necessary to identify and understand the different choices that are made during the process of classifying vegetation. This paper presents a framework to facilitate comparisons between broad-scale plot-based classification approaches. Results: Our framework is based on the distinction of four structural elements (plot record, vegetation type, consistent classification section and classification system) and two procedural elements (classification protocol and classification approach). For each element we describe essential properties that can be used for comparisons. We also review alternative choices regarding critical decisions of classification approaches; with a special focus on the procedures used to define vegetation types from plot records. We illustrate our comparative framework by applying it to different broad-scale classification approaches. Conclusions: Our framework will be useful for understanding and comparing plot-based vegetation classification approaches, as well as for integrating classification systems and their sections. We present a comparison framework for vegetation classification that distinguishes four structural elements (plot record, vegetation type, consistent classification section and classification system) and two procedural elements (classification protocol and classification approach). The framework will be useful for understanding and comparing plot-based vegetation classification approaches, as well as for integrating classification systems and their sections. © 2015 International Association for Vegetation Science

Bibliometric analysis of scientific production on methods to aid decision making in the last 40 years

Purpose: Multicriteria methods have gained traction in both academia and industry practices for effective decision-making over the years. This bibliometric study aims to explore and provide an overview of research carried out on multicriteria methods, in its various aspects, over the past forty-four years. Design/Methodology/Approach: The Web of Science (WoS) and Scopus databases were searched for publications from January 1945 to April 29, 2021, on multicriteria methods in titles, abstracts, and keywords. The bibliographic data were analyzed using the R bibliometrix package. Findings: This bibliometric study asserts that 29,050 authors have produced 20,861 documents on the theme of multicriteria methods in 131 countries in the last forty-four years. Scientific production in this area grows at a rate of 13.88 per year. China is the leading country in publications with 14.14%; India with 10.76%; and Iran with 8.09%. Islamic Azad University leads others with 504 publications, followed by the Vilnius Gediminas Technical University with 456 and the National Institute of Technology with 336. As for journals, Expert Systems With Applications; Sustainability; and Journal of Cleaner Production are the leading journals, which account for more than 4.67% of all indexed literature. Furthermore, Zavadskas E. and Wang J have the highest publications in the multicriteria methods domain regarding the authors. Regarding the most commonly used multicriteria decision-making methods, AHP is the most favored approach among the ten countries with the most publications in this research area, followed by TOPSIS, VIKOR, PROMETHEE, and ANP. Practical implications: The bibliometric literature review method allows the researchers to explore the multicriteria research area more extensively than the traditional literature review method. It enables a large dataset of bibliographic records to be systematically analyzed through statistical measures, yielding informative insights. Originality/value: The usefulness of this bibliometric study is summed in presenting an overview of the topic of the multicriteria methods during the previous forty-four years, allowing other academics to use this research as a starting point for their research

The State of the Art in Cartograms

Cartograms combine statistical and geographical information in thematic maps, where areas of geographical regions (e.g., countries, states) are scaled in proportion to some statistic (e.g., population, income). Cartograms make it possible to gain insight into patterns and trends in the world around us and have been very popular visualizations for geo-referenced data for over a century. This work surveys cartogram research in visualization, cartography and geometry, covering a broad spectrum of different cartogram types: from the traditional rectangular and table cartograms, to Dorling and diffusion cartograms. A particular focus is the study of the major cartogram dimensions: statistical accuracy, geographical accuracy, and topological accuracy. We review the history of cartograms, describe the algorithms for generating them, and consider task taxonomies. We also review quantitative and qualitative evaluations, and we use these to arrive at design guidelines and research challenges

National innovative capacity: An established concept revisited

National innovative capacity, a central driver of countries’ long-term economic growth, has been one of the focal points in innovation research for roughly thirty years. Initially proposed as an index to measure technologic invention over time, this concept has become the widely accepted standard for measuring the performance of (sub) national and sectoral innovation systems toward being an analytic tool attributed to innovation systems theory. Country comparison, knowledge flows, and R&D forecasting are in the center of analysis feeding the concrete practical use of innovation policy optimization. In this regard, a rich body of studies has contributed indispensable knowledge about the determinants of innovative capacity. However, the multi-dimensional interconnections have not been covered in depth. Thus, to gain a holistic understanding of the “DNA” behind national innovative capacity a new “comparative” view of these determinants is necessary. To this end, this dissertation proposes revisiting the focus, unit and parameters of analysis that predominate within current national innovative capacity studies and sets forth three interlinked academic articles that focus on different layers of innovative capacity in countries. Besides furthering academic discourse on the determinants of innovational outcome, this conceptual revision leads to a new approach on national innovation capacity research. Its intention is to make policy makers aware of certain pathways leading to the same outcome. This knowledge will enable them to pursue a dynamic approach of supporting the innovative processes in countries by defining appropriate innovation strategies that consider both the countries’ specific preconditions and the sub-systems perspective.:1. Introduction 2. The purpose of revisiting the NIC concept for innovation policy 3. The scientific contribution of this doctoral thesis 3.1 Article 1: Increasing the national innovative capacity: Identifying the pathways to success using a comparative method 3.2 Article 2: National Health Innovation Systems: Clustering the OECD countries by innovative output in healthcare using a multi-indicator approach 3.3 Article 3: Increasing the innovative capacity of European cities: Making use of proven concepts from the national level 4. Reference

A bibliometric review of the technology transfer literature

This study explores academic research on technology transfer (TT) and the related themes. The TT field has attracted considerable scholarly attention in recent years and has grown rapidly, resulting in a large body of knowledge. Using a bibliometric approach, this study reviews related research issues as well as their influence and connections and provides directions for future research. It uses Clarivate Analytics’ Web of Science database that includes 3,218 bibliographic references. Several bibliometric analysis techniques and a subsequent review of the content of the most relevant documents are adopted. The performance analysis provided an updated overview of the evolution of the TT literature from 1969 to 2018 and quantitatively identified the most active and influential journals, articles, authors, and organizations. The co-authorship network analysis allowed us to identify and visualize the structure of relations between authors as well as determine the collaboration patterns among them. On the basis of the information supplied by the co-authorship network, the main literature was reviewed to identify the current status and research trends related to TT, identifying five main research streams and related topics. The implications of the study’s findings and directions for future TT research are finally discussed to enhance our understanding of TT agents and issues and support further research in this field.Funding for this research was provided by the Spanish Ministry of Science, Innovation & Universities (MCIU/AEI/FEDER-UE) under the Grant Number RTI2018-097579-B-100, by UPV/EHU under the Grant Number GIU16/46, and by FESIDE