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Ontologies are prone to wide semantic variability due to subjective points of view of their composers. The purpose of this paper is to propose a new approach for maximal unification of diverse ontologies for controversial domains by their relations.

Effective matching or unification of multiple ontologies for a specific domain is crucial for the success of many semantic web applications, such as semantic information retrieval and organization, document tagging, summarization and search. To this end, numerous automatic and semi-automatic techniques were proposed in the past decade that attempt to identify similar entities, mostly classes, in diverse ontologies for similar domains. Apparently, matching individual entities cannot result in full integration of ontologies’ semantics without matching their inter-relations with all other-related classes (and instances). However, semantic matching of ontological relations still constitutes a major research challenge. Therefore, in this paper the authors propose a new paradigm for assessment of maximal possible matching and unification of ontological relations. To this end, several unification rules for ontological relations were devised based on ontological reference rules, and lexical and textual entailment. These rules were semi-automatically implemented to extend a given ontology with semantically matching relations from another ontology for a similar domain. Then, the ontologies were unified through these similar pairs of relations. The authors observe that these rules can be also facilitated to reveal the contradictory relations in different ontologies.

To assess the feasibility of the approach two experiments were conducted with different sets of multiple personal ontologies on controversial domains constructed by trained subjects. The results for about 50 distinct ontology pairs demonstrate a good potential of the methodology for increasing inter-ontology agreement. Furthermore, the authors show that the presented methodology can lead to a complete unification of multiple semantically heterogeneous ontologies.

This is a conceptual study that presents a new approach for semantic unification of ontologies by a devised set of rules along with the initial experimental evidence of its feasibility and effectiveness. However, this methodology has to be fully automatically implemented and tested on a larger dataset in future research.

This result has implication for semantic search, since a richer ontology, comprised of multiple aspects and viewpoints of the domain of knowledge, enhances discoverability and improves search results.

To the best of the knowledge, this is the first study to examine and assess the maximal level of semantic relation-based ontology unification.

The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.

The detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.

The paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.

The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

Ontologies are defined as consensual formal conceptualisation of shared knowledge. However, the explicit overlap between diverse ontologies is usually very low since they are typically constructed by different experts. Hence, the purpose of this paper is to suggest to exploit “wisdom of crowds” to assess the maximal potential for inter-ontology agreement on controversial domains.

The authors propose a scheme where independent ontology users can explicitly express their opinions on the specified set of ontologies. The collected user opinions are further employed as features for machine classification algorithm to distinguish between the consensual ontological relations and the controversial ones. In addition, the authors devised new evaluation methods to measure the reliability and accuracy of the presented scheme.

The accuracy of the relation classification (90 per cent) and the reliability of user agreement annotations were quite high (over 90 per cent). These results indicate a fair ability of the scheme to learn the maximal set of consensual relations out of the specified set of diverse ontologies.

The data sets and the group of participants in our experiments were of limited size and thus the presented results are promising but cannot be generalised at this stage of research.

A diversity of opinions expressed by different ontologies has to be resolved in order to digitise many domains of knowledge (e.g. cultural heritage, folklore, medicine, economy, religion, history, art). This work presents a methodology to formally represent this diverse knowledge in a rich semantic scheme where there is a need to distinguish between the commonly shared and the controversial relations.

To the best of the knowledge this is a first proposal to consider crowd-based evaluation and classification of ontological relations to maximise the inter-ontology agreement.

To analyse common metaphors used in the intellectual capital (IC) and knowledge management literatures to conceptualise knowledge, in order to study the nature of the intellectual capital concept.

A textual analysis methodology is used to analyse texts from The Knowledge‐Creating Company by Nonaka and Takeuchi, Working Knowledge by Davenport and Prusak and “Brainpower” by Stewart, in order to identify underlying metaphors.

Over 95 per cent of the statements about knowledge identified are based on some kind of metaphor. The two dominant metaphors that form the basis for the concept of intellectual capital are “knowledge as a resource” and “knowledge as capital”.

Metaphors highlight certain characteristics and ignore others, so the IC community should ask itself what characteristics of knowledge the “knowledge as a resource” and “knowledge as capital” metaphors ignore.

Knowledge has no referent in the real world and requires metaphor to be defined, conceptualised, and acted upon. When using such metaphors we should become aware of their limitations as they steer us in certain directions and this may happen unconsciously. The paper concludes by asking whether we need new metaphors to better understand the mechanisms of the knowledge economy, hence allowing us to potentially change some of the more negative structural features of contemporary society.

This paper is the first to highlight that intellectual capital is a metaphor and that the metaphorical nature of the concept has far reaching consequences.

The purpose of this paper is to discuss a Multi-perspective approach to knowledge production in terms of a set of cybernetic concepts relevant to the approach; to describe a software system that computationally embodies the approach; and to articulate a research project that pragmatically employs the approach.

A definition is provided. The paper uses a survey methodology, exploring relevant cybernetic and contemporary technological concepts. An operational software mechanism (The Insight Engine) is discussed that enables the bridging of transdisciplinary concepts by a user in the service of accretive research –Recombinant Informatics.

Many cybernetic concepts are relevant to contemporary research into cognition and Neosentience research. More study needs to be undertaken related to historical BCL projects in terms of articulating relevance to contemporary research.

Future research seeks to extend the computational functionality of “The insight engine”, as well as uncover relevant BCL/cybernetic materials.

The software is unique in the field and already there is interest in its use by differing research communities including the Duke Institute for Brain Sciences, and at Stanford, research under Ian Hodder.

The Insight Engine has potential to be used as a multi-perspective tool for many different fields enabling different forms of distributed, transdisciplinary team-based research.

This text is valuable to researchers interested in new forms of interface, augmentation of thought and learning via computational approaches; and the development of bridges between novel research areas, including contemporary, historical BCL, and other cybernetic inquiry.

The purpose of this paper is to review the use of adaptive systems in education. It is intended to be a useful introduction for the non-specialist reader.

A distinction is made between intelligent tutoring systems (ITSs) and adaptive hypermedia systems (AHSs). The two kinds of system are defined, compared and contrasted. Examples of the implementation of the two kinds of system are included.

Similarities and differences between the two kinds of system are highlighted. A conceptual unification is proposed based on the architecture of Course Assembly System and Tutorial Environment, a seminal prototype learning environment developed by Pask and Scott in the 1970s as an application of Pask’s conversation theory.

The architecture shows how the key aspects of ITSs and AHSs can be combined to complement each other. It is intended to be an original contribution that is of particular interest for the specialist reader.

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

Describes Byzantium, an intelligent tutoring system for teaching the concepts and skills of accounting. The generic design philosophy of Byzantium and its associated intelligent tutoring tools are described, together with commentary that places Byzantium in the tradition of the adaptive teaching machines and conversational tutorial systems (SAKI and CASTE) developed by Gordon Pask.

Arts-informed, visual research was conducted to document the pictorial metaphors that appear among original drawings of information. The purpose of this paper is to report the diversity of these pictorial metaphors, delineate their formal qualities as drawings, and provide a fresh perspective on the concept of information.

The project utilized pre-existing iSquare drawings of information that were produced by iSchool graduate students during a draw-and-write activity. From a data set of 417 images, 125 of the strongest pictorial metaphors were identified and subjected to cognitive metaphor theory.

Overwhelmingly, the favored source domain for envisioning information was nature. The most common pictorial metaphors were: Earth, web, tree, light bulb, box, cloud, and fishing/mining, and each brings different qualities of information into focus. The drawings were often canonical versions of objects in the world, leading to arrays of pictorial metaphors marked by their similarity.

Less than 30 percent of the data set qualified as pictorial metaphors, making them a minority strategy for representing information as an image. The process to identify and interpret pictorial metaphors was highly subjective. The arts-informed methodology generated tensions between artistic and social scientific paradigms.

The pictorial metaphors for information can enhance information science education and fortify professional identity among information professionals.

This is the first arts-informed, visual study of information that utilizes cognitive metaphor theory to explore the nature of information. It strengthens a sense of history, humanity, nature, and beauty in our understanding of information today, and contributes to metaphor research at large.