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In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

This paper reports on the design of a knowledge base for an automatic classification in the library science field, by using the facet classification principles of colon classification (CC). To do so, by designing and constructing a knowledge base that is able to be classified automatically, and by inputting titles or key words of volumes into the computer, it aims to create class numbers automatically through automatic subject recognition and processing of key words in titles through the facet combination method of CC. Especially, the knowledge base for classification was designed along with the principle of globe and cylinder, automatic classification which can be possible.

The purpose of this paper is to provide an answer to a critique put forward by Szostak against a paper written by the present author.

The paper is based on a literature‐based conceptual analysis based on Hjørland and Nissen Pedersen and Szostak. The main points in a core theory of classification are outlined and Szostak's criticism is examined and answered.

The paper demonstrates theoretical differences between the views adduced by Hjørland and Nissen Pedersen on the one side and by Szostak on the other.

Theoretical clarification is important for the future development of the field.

The paper should be seen as one among others developing an argument for a theoretical foundation of classification informed by the theory of knowledge.

The emerging area of message classification is one of growing relevance to a wide range of organizational communicators as a variety of non-state organizations and their members increasingly use and misuse various terms to restrict their communication. This includes formal classifications for data security, financial/knowledge management, human resources, and other functions as well as those used informally by organizational members. Especially in a data-rich environment where our word-processing programs, e-mail tools, and other technologies afford us opportunities to engage in classification, a wide range of people at all organizational levels may serve as custodians of their own data and thus have the ability (as well as perhaps the need) to classify messages in various ways. The purpose of this paper is to describe key classification terms ranging from those found in government (e.g. top secret, confidential) to those in the private sector (e.g. business use only, trademarked) to an even wider set of terms used informally by organizational members (e.g. personal, preliminary). The growing use of message classifications will likely create various challenges and opportunities for organizations, their members, and the broader public/society. A set of future research questions is offered for corporate communication researchers and practitioners, who are well positioned to examine this emerging phenomenon.

This paper draws on existing literature related to the growing use of message classifications to offer a list of classification terms and an agenda for future research.

This work describes key classification terms ranging from those found in government (e.g. top secret, confidential) to those in the private sector (e.g. business use only, trademarked) to an even wider set of terms used informally by organizational members (e.g. personal, preliminary). This expanded notion of classification will likely create various challenges and opportunities for organizations, their members, and the broader public/society.

The emerging area of message classification is one of growing relevance to a wide range of organizational communicators as a variety of non-state organizations and their members increasingly use and misuse various terms to restrict their communication. A set of future research questions is offered for corporate communication researchers and practitioners, who are well positioned to examine this emerging phenomenon.

With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.

This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.

Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.

This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

Hotel classification systems are used to convey information about facilities and services. Yet, they have been prone to criticism for overemphasizing facilities at the expense of other matters of importance to service quality. In contrast, online travel agents (OTAs) use innovative methods to evaluate satisfaction with hotels. Conventional systems will lose relevance if they do not step up to consider service aspects associated with customer satisfaction. This chapter probes five hotel classification systems along with one OTA and leverages the literature to propose an improved framework classification. This is based on nine critical areas that include service quality, infrastructure, facilities and services, human resources, sustainability, safety and security, accessibility, quality systems, and online hotel ratings.

When decision makers encounter new assurance services that can be customized for individual clients, they must include them in their pre-existing categorization of assurance, a cognitive task known as postclassification. This paper draws upon three literatures (classification research in accounting, theory of assurance, and cognitive psychology) in order to suggest how this task might be modeled and studied empirically, using the example of SysTrust™. The role of a necessary condition for successful postclassification called the category use effect (Ross, 2000), in which decision makers are reminded of pre-existing categories when they learn to use new categories, is explained.

Books serve as important information resources and provide space for reflection and identity-building for many lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+) people. Many in this community have experienced reduced feelings of isolation through engagement with the writings of others. Providing a safe space for such engagement is vital. Library and information science (LIS) professionals are in an optimal position to meet such needs, particularly when efforts are made to implement changes based on explicitly expressed concerns.

This chapter provides a case study of the LGBTQ Center of Durham, North Carolina, to illustrate how the organization is integrating the local LGBTQ+ community into its library by using the community’s own vocabulary and interests to inform the center’s practices and policies. The chapter also offers a guide to the locally responsive, LGBTQ+-specific classification system created for the LGBTQ Center of Durham’s library collection. This classification system was designed to represent library materials for its Durham and surrounding-area users in a useful, accessible, and respectful manner – a feat that the library committee did not feel could be accomplished using existing classification systems.

Building on the case study for applicability, the author makes recommendations for how LIS professionals who wish to better serve LGBTQ+ users can incorporate the community into their library and/or collection. The author provides additional suggestions for action, with varying levels of commitment, for library professionals and volunteers. Through resource development, training, collection development, and classification revision, libraries can more closely align their practices with the needs of users of all gender identities and sexual orientations.

This chapter reviews the historical tension between global and local interests in library classifications. More specifically, this chapter presents the concept and characteristics of the reader-interest classifications as they were reported in the literature of the past century, including its alleged advantages and detected shortcomings, in order to discuss their presence and consequences in current cases of reader-interest classifications such as BISAC. Following an implicit post-structuralist approach, issues such as the role of standardization and centralization in these projects, the focus and philosophy underlying the construction of these classifications, and the underlying global interests of the book industry are analyzed in order to determine the social consequences and viability of these local classifications. It is concluded that libraries that consider adopting a reader-interesting classification must really think of the interest of the users (in plural) and not only of the global book industry that dominates the development of the standards.