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This paper examines a user categorisation of documents related to a particular literary work. Fifty study participants completed an unconstrained sorting task of documents related to Charles Dickens’ A Christmas carol. After they had finished the sorting task, participants wrote descriptions of the attributes they used to create each group. Content analysis of these descriptions revealed categories of attributes used for grouping. Participants used physical format, audience, content description, pictorial elements, usage, and language most frequently for grouping. Many of the attributes participants used for grouping already exist in bibliographic records and may be used to cluster records related to works automatically in online catalogue displays. The attributes used by people in classifying or grouping documents related to a work may be used to guide the design of summary online catalogue work displays.

Identifying the fundamental characteristics of meaning and deriving an automated meaning‐analysis procedure for machine intelligence.

Semantic category theory (SCT) is an original testable scientific theory, based on readily available data: not assumptions or axioms. SCT can therefore be refuted by irreconcilable data: not opinion.

Human language involves four totally independent semantic categories (SC), each of which has its own distinctive form of “Truth”. Any sentence that assigns the characteristics of one SC to another SC involves what is termed here “Semantic Intertwine”. Semantic intertwine often lies at the core of semantic ambiguity, sophistry and paradox: problems that have plagued human reason since antiquity.

SCT is applicable to any endeavour involving human language. Research applications are therefore somewhat extensive. For example, identifying metaphors posing as science, or natural language processing/translation, or solving disparate paradox types, as illustrated by worked examples from: The Liar Group, Sorites Inductive, Russell's Set Theoretic and Zeno's Paradoxes.

To interact successfully with human language, behaviour, and belief systems, as well as their own environment, intelligent machines will need to resolve the semantic component/intertwines of any sentence. Semantic category analysis (SCA), derived from SCT, and also described here, can be used to analyse any sentence or argument, however complex.

Both SCT and SCA are original. Whilst “category error” is an intuitive notion, the observably precise nature, number and modes of interaction of such categories have never previously been presented. With SCT/SCA the rigorous analysis of any argument, whether foisted, valid, or obfuscating, is now possible: by man or machine.

The purpose of this paper is to survey the treatment of relationships, relationship expressions and the ways in which they manifest themselves in image descriptions.

The term “relationship” is construed in the broadest possible way to include spatial relationships (“to the right of”), temporal (“in 1936,” “at noon”), meronymic (“part of”), and attributive (“has color,” “has dimension”). The intentions of these vaguely delimited categories with image information, image creation, and description in libraries and archives is complex and in need of explanation.

The review brings into question many generally held beliefs about the relationship problem such as the belief that the semantics of relationships are somehow embedded in the relationship term itself and that image search and retrieval solutions can be found through refinement of word-matching systems.

This review has no hope of systematically examining all evidence in all disciplines pertaining to this topic. It instead focusses on a general description of a theoretical treatment in Library and Information Science.

This paper aims to propose an automated mobile app labeling framework based on a novel app classification scheme that is aligned with users’ primary motivations for using smartphones. The study addresses the gaps in incorporating the needs of users and other context information in app classification as well as recommendation systems.

Based on a corpus of mobile app descriptions collected from Google Play store, this study applies extensive text analytics and topic modeling procedures to profile mobile apps within the categories of the classification scheme. Sufficient number of representative and labeled app descriptions are then used to train a classifier using machine learning algorithms, such as rule-based, decision tree and artificial neural network.

Experimental results of the classifiers show high accuracy in automatically labeling new apps based on their descriptions. The accuracy of the classification results suggests a feasible direction in facilitating app searching and retrieval in different Web-based usage environments.

As a common challenge in textual data projects, the problem of data size and data quality issues exists throughout the multiple phases of experiments. Future research will extend the data collection scope in many aspects to address the issues that constrained the current experiments.

These empirical experiments demonstrate the feasibility of textual data analysis in profiling apps and user context information. This study also benefits app developers by improving app descriptions through a better understanding of user needs and context information. Finally, the classification framework can also guide practitioners in customizing products and services beyond mobile apps where context information and user needs play an important role.

Given the widespread usage and applications of smartphones today, the proposed app classification framework will have broader implications to different Web-based application environments.

While there have been other classification approaches in the literature, to the best of the authors’ knowledge, this framework is the first study on building an automated app labeling framework based on primary motivations of smartphone usage.

The study aimed to examine how different communities concerned with dementia engage and interact on Twitter.

A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.

Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.

The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.

One of the open questions is how to ensure the conceptual adaptation in the pervasive system. To answer this question, the authors needed to propose a generic model and a mechanism to describe this system and also need generic and semantic rules to ensure the adaptation. This paper aims to propose a model to describe the pervasive information system. Second, the authors suggest an approach to divide this model so as to describe each category of contextual information separately and ensure the adaptation in the pervasive system. Finally, the authors present examples of semantic rules executed in the pervasive system.

This paper proposes an approach to ensure the conceptual adaptation in the pervasive system. To do it, the authors proposed a model to design the pervasive system and used semantic Web services. They proposed to divide the model to six descriptions related to the pervasive system categories information.

Pervasive information system, conceptual adaptation, semantic Web services and OWL-S are presented in this paper.

The originality of this paper is presented in the purpose of the pervasive information system conceptual adaption in the pervasive system. In this, later, semantic Web services were used to ensure the adaptation by the adding of contextual information in the semantic Web service description.

This paper aims to present the results of a study that investigated the Erasmus Mundus Digital Library Learning (DILL) Master programme students' conceptions and experiences of the use of Web 2.0 tools.

The study adopted phenomenography as a research approach to identify DILL students' conceptions and experiences of Web 2.0 tools. Semi‐structured interviews with open‐ended questions were conducted with 12 students from Africa and Asia within the DILL Master programme.

The data analysis revealed four categories of descriptions of Web 2.0 tools: communication, educational, professional and multi‐purpose. For each category of descriptions preferred Web 2.0 tools were identified.

The study analyses only conceptions and experiences of the use of Web 2.0 tools of 12 DILL students. This small group of students was from Africa and Asia and, therefore, the results should not be generalized to describe all DILL students' conceptions and experiences of the use of Web 2.0 tools.

The results of this study can be taken into consideration when designing and delivering a DILL programme. In order to use technologies to support learning there is a need to understand and know what students do with these new technological tools.

This paper supports the idea of integration of information and communication technologies into education and highlights the potential of Web 2.0 tools to support teaching and learning in the higher education setting.

The purpose of this paper is to identify the potential business opportunities for Middle Eastern entrepreneurs by understanding the Malaysian Muslim’s ways of experiencing and realising value of products originating from Middle Eastern countries.

Phenomenography approach has been used to identify the variations among the Muslim consumers’ ways of experiencing Middle Eastern products.

The authors reported that Muslim consumers considered the country of origin as an important cue that affects their knowing, understanding, judging and acting on products originating from Middle Eastern countries.

Understanding developed from the Malaysian Muslim consumers enabled authors to suggest business opportunities for Middle Eastern entrepreneurs to enter and expand their operations in other leading Islamic countries.

In this description of phenomenography, we take a functional view of the theoretical underpinnings that have traditionally been used to support its trustworthiness as a qualitative research approach. The chapter has two objectives, first to serve as an introduction for those considering embarking on research with a phenomenographic framing, and second to enable the recognition of the quality and scope of the knowledge claim inherent in phenomenographic outcomes.

The purpose of this paper is to describe different collective ways that archivists, librarians and those with dual-roles experience archives.

Using a phenomenographic approach, a total of 24 librarians, archivists and dual-role individuals were interviewed, and interview transcripts were analyzed to create categories that described the varying ways in which archives are experienced.

Librarians experienced archives in four ways: historical resources, preserving history, preserved access and political. Archivists experienced archives in four ways: accessible collections, connection, collaboration and stewardship. Dual-role individuals experienced archives in five ways: collections, preserved access, progress, connection and knowledge creators. There are variations among and within each group on how archives are experienced. However, there is a significant overlap in many categories in terms of access, preservation, use and collections.

Understanding each other’s different perspectives could lead to stronger partnerships among librarians, archivists and dual-role individuals. These partnerships have the potential to increase the visibility of archives, providing greater access and engagement for community members.

The study supports previous phenomenographic research on experiences of archives and provides a more nuanced understanding of information professionals’ varying collective experiences of archives.