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The need for inclusive and logically consistent representation of diverse and even confronting viewpoints on the domain knowledge has been widely discussed in the literature in the past decade. The purpose of this paper is to propose a generic model for building an open coherent diversified knowledge organization system (KOS).

The proposed model incorporates a generic epistemological component, the validity scope type, assigned to each statement in the constructed KOS. Statements are clustered by their association with various validity scope types into internally coherent subsystems. These subsystems form a knowledge organization network connected through the universal (consensual) subsystems with more than one validity scope type. The model extends the Galili’s Cultural Content Representation paradigm, which divides the knowledge content of a scientific theory into two confronting parts: body and periphery.

The knowledge organization network model makes it possible to comparatively examine similarities and differences among various viewpoints and theories on the domain knowledge. The presented approach conforms with the principle of Open Knowledge Network initiative for creation of open accessible knowledge.

The proposed model can be used for ontological reasoning by a variety of information services, such as ontology-based decision-support and learning systems, diversified search and customer management applications.

The model enables explicit representation of social and cultural minority voices and historical knowledge in the KOS.

The main contribution of the proposed model is that it generalizes and enhances various previously proposed representations of epistemological aspects of KOS and allows for multiple inter-linked subsystems to coherently co-exist as part of the extensible network.

The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design.

An ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, bagging and random subspace) using 11 text benchmarks.

The experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification.

The presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification

This paper aims to discuss how collaborative classification works in online music information retrieval systems and its impacts on the construction, fixation and orientation of the social uses of popular music on the internet.

Using a comparative method, the paper examines the logic behind music classification in Recommender Systems by studying the case of Last.fm, one of the most popular web sites of this type on the web. Data collected about users' ritual classifications are compared with the classification used by the music industry, represented by the AllMusic web site.

The paper identifies the differences between the criteria used for the collaborative classification of popular music, which is defined by users, and the traditional standards of commercial classification, used by the cultural industries, and discusses why commercial and non‐commercial classification methods vary.

Collaborative ritual classification reveals a shift in the demand for cultural information that may affect the way in which this demand is organized, as well as the classification criteria for works on the digital music market.

Collective creation of a music classification in recommender systems represents a new model of cultural mediation that might change the way of building new uses, tastes and patterns of musical consumption in online environments.

The paper highlights the way in which the classification process might influence the behavior of the users of music information retrieval systems, and vice versa.

The purpose of this paper is to solve the problem of information privacy and security of social users. Mobile internet and social network are more and more deeply integrated into people’s daily life, especially under the interaction of the fierce development momentum of the Internet of Things and diversified personalized services, more and more private information of social users is exposed to the network environment actively or unintentionally. In addition, a large amount of social network data not only brings more benefits to network application providers, but also provides motivation for malicious attackers. Therefore, under the social network environment, the research on the privacy protection of user information has great theoretical and practical significance.

In this study, based on the social network analysis, combined with the attribute reduction idea of rough set theory, the generalized reduction concept based on multi-level rough set from the perspectives of positive region, information entropy and knowledge granularity of rough set theory were proposed. Furthermore, it was traversed on the basis of the hierarchical compatible granularity space of the original information system and the corresponding attribute values are coarsened. The selected test data sets were tested, and the experimental results were analyzed.

The results showed that the algorithm can guarantee the anonymity requirement of data publishing and improve the effect of classification modeling on anonymous data in social network environment.

In the test and verification of privacy protection algorithm and privacy protection scheme, the efficiency of algorithm and scheme needs to be tested on a larger data scale. However, the data in this study are not enough. In the following research, more data will be used for testing and verification.

In the context of social network, the hierarchical structure of data is introduced into rough set theory as domain knowledge by referring to human granulation cognitive mechanism, and rough set modeling for complex hierarchical data is studied for hierarchical data of decision table. The theoretical research results are applied to hierarchical decision rule mining and k-anonymous privacy protection data mining research, which enriches the connotation of rough set theory and has important theoretical and practical significance for further promoting the application of this theory. In addition, combined the theory of secure multi-party computing and the theory of attribute reduction in rough set, a privacy protection feature selection algorithm for multi-source decision table is proposed, which solves the privacy protection problem of feature selection in distributed environment. It provides a set of effective rough set feature selection method for privacy protection classification mining in distributed environment, which has practical application value for promoting the development of privacy protection data mining.

In this study, the proposed algorithm and scheme can effectively protect the privacy of social network data, ensure the availability of social network graph structure and realize the need of both protection and sharing of user attributes and relational data.

This chapter aims to investigate the research status and development of virtual community (VC) in China by a critique of library and information science (LIS) journal articles and to put forward future directions in virtual community for the researchers in our discipline. We used a multiple case of methods including bibliometric approaches (such as keyword frequency and co-word analysis) and a coding system to reveal several characteristics of the research status quo and trends. The results show that from 2000 to the present, VC-related research by LIS Chinese scholars has experienced rapid development with an increasing number of academic papers, authors, journals and institutions. However, LIS research is still at an early stage with the slow introduction of fundamental concepts and frameworks, lack of theoretical support, and organisations focused on empirical studies. VC-related research in China in recent years fully demonstrates its diversified attributes. Our study is limited to the analysis of academic journal literature published in mainland China while excluding dissertations and books, and publications in Hong Kong, Macao and Taiwan. Our classification system also needs to be more specific. Our findings have implications for researchers, students, journals and sponsors of Chinese LIS research related to virtual communities.

Knowledge organization (KO) has been advancing at a progressively rapid pace under the influence of information technology. This study aims to explore the topics, characteristics, and trends of KO research in the 21st century.

The full text of 4,360 KO-related articles published from 2000 to 2021 is collected. Through content analysis, this study identifies the topics, research methods, and application areas of each article, and the statistics are presented through a series of visualizations.

In total, 13 main topics, 105 sub-topics, 16 research methods, and 57 application areas are identified. Notably, classification has always been an important topic, while linked data, automated techniques, and ontology have become popular topics recently. Significant changing features have also occurred. The versatile use of research methods has increased, with empirical research becoming the mainstream. Application areas show a trend of refinement from subject areas to specific scenarios. Construction techniques present a combination of automated techniques, crowdsourcing, and experts.

KO has evolved and diversified due to technological developments. This study is the first to focus on the continuous changing features over an extended, 21-year period, as opposed to sampling a few years. It also provides clues and insights for researchers and practitioners interested in KO to understand how it has changed in the Semantic Web and big data context.

The energy efficiency policy is a priority component of the overall economic policy of different countries striving to ensure the competitiveness and sustainability of national economic development. The improvement of energy efficiency represents an important economic task for the post-Soviet countries, characterized by excessive energy intensity of the economy, and the solution of this task requires proper information and analytical support: a system for accounting and analyzing energy consumption indicators. The present research is aimed at developing the tools to support decision-making in the sphere of evaluation and estimation of performance of the State energy efficiency policy of territories and testing these tools on the example of Russian regions. The study has been carried out using the methods of statistics, economic, mathematical and econometric modeling, structural, dynamic and comparative analyses. The following tools have been proposed: the method for differentiated accounting of various factors’ influence on the dynamics of energy consumption in the regions and for estimating the index of technological efficiency of electricity consumption; the method for the empirical classification of territories by types of their energy and economic development. We’ve revealed the general trend and typological features in the dynamics of electricity consumption efficiency indicators in the constituent entities of the Russian Federation and carried out the decomposition factor and comparative analysis of energy consumption patterns of the Volgograd region over 2005–2014 on the basis of the proposed tools.

The authors study the valuation effect of corporate diversification in the initial phase of the COVID-19 pandemic in 2020 in Europe.

Applying a cross-sectional regression model to a sample of public companies headquartered in the European Union, the authors investigate the existence of and the change in a diversification discount between 2018 and 2020. By applying the Excess Q methodology, the authors make an industry adjustment of diversified companies to measure the value effect of corporate diversification.

The authors find an economically and statistically significant diversification discount that increases from an average Excess Q of −0.05 in 2019 to −0.10 in 2020. The diversified companies' inferior fundamental financial performance in 2020 accompanies the discount. The results deviate from those of previous research, which mostly show a decrease in the diversification discount in economic crises, and thereby, shed doubt on whether diversification provides insurance against pandemic-induced adverse value effects.

The study distinguishes the role of corporate diversification during recessionary periods by establishing that the valuation effect of diversification depends on the nature of the crisis. The analysis incorporates criticism of previous studies concerning a biased methodology and uniform data source by applying the Excess Q methodology and using FactSet industry segment data.

The conditions that domain analysis becomes an academic school of information science (IS) are mature. Domain analysis is one of the most important foundations of IS. The purpose of this paper is to analyze and discuss metatheoretical and theoretical issues in the domain analytic paradigm in IS.

This paper conducts a systematic review of representative publications of domain analysis. The analysis considered degree theses, journal articles, book chapters, conference papers and other materials.

Domain analysis maintains that community is the new focus of IS research. Although domain analysis centers on the domain and community, theoretical concerns on the social and individual dimensions of IS are inherent in it by its using sociology as its important approach and socio-cognitive viewpoint. For these reasons domain analysis can integrate social–community–individual levels of IS discipline as a whole. The role of subject knowledge in IS is discussed from the perspective of domain analysis. Realistic pragmatism that forms the philosophical foundation of domain analysis is argued and the implications of these theories to IS are presented.

The intellectual evolving landscape of domain analysis during a quarter century is comprehensively reviewed. Over the past twenty-five years, domain analysis has established its academic status in the international IS circle. Being an important metatheory, paradigm and methodology, domain analysis becomes the theoretical foundation of IS research. This paper assesses the current state of domain analysis and shows the contributions of domain analysis to IS. It also aims to inspire further exploration.