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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.

The purpose of the article is to create a knowledge classification model that can be used by knowledge management (KM) practitioners for establishing a knowledge management framework (KMF) in a supply chain (SC) network. Epistemological and ontological aspects of knowledge have been examined. SC networks provide a more generic setting for managing knowledge due to the additional issues concerning flow of knowledge across the boundaries of organizations.

Morphological analysis has been used to build the knowledge classification model. Morphological approach is particularly useful in exploratory research on concepts/entities having multiple dimensions. Knowledge itself has been shown in literature to have many characteristics, and the methodology used has enabled a comprehensive classification scheme based on such characteristics.

A single comprehensive classification model for knowledge that exists in SC networks has been proposed. Nine characteristics, each possessing two or more value options, have been finally included in the model.

Knowledge characteristics have been mostly derived from past research with the exception of three which have been introduced without empirical evidence. Although the article is primarily about SC knowledge, the results are fairly generic.

The proposed model would be of use in developing KM policies, procedures and establishing knowledge management systems in SC networks. The model will cater to both system and people aspects of a KMF.

The proposed knowledge classification model based on morphological analysis fills a gap in a vital area of research in KM as well as SC management. No similar classification model of knowledge with all its dimensions has been found in literature.

The purpose of this paper is to establish pluralism as the basis for bibliographic classification theory and practice and examine the possibility of establishing trustworthy classifications.

The paper examines several key notions in classification and extends previous frameworks by combining an explanation‐based approach to classification with the concepts of cognitive authority and trust.

The paper presents an understanding of classification that allows designers and editors to establish trust through the principle of transparency. It demonstrates that modern classification theory and practice are tied to users' activities and domains of knowledge and that trustworthy classification systems are in close dialogue with users to handle bias responsible and establish trust.

The paper establishes a foundation for exploring trust and authority for classification systems.

This study attempts to formulate an ontological proposition for the intellectual capital (IC) domain. This study is motivated by the debate of contemporary thinking for different IC research streams (IC1‐ostensive versus IC2‐performative) and their different ontological perceptions for IC. The proposed ontological proposition aims to serve the epistemological requirements towards the development of a common accepted generic IC theory.

The formulation of the ontology for the IC domain is based on contemporary tools within the fields of ontology and conceptual modelling. The principal dimensions of the ontology for the IC domain are identified following the directions of the Sowa's generalised theory for ontological frameworks. For each dimension, a generic entity is defined by drawing inferences from disciplines relevant to the contemporary IC inquiry. The relationships between the generic entities are modelled with the semantic representations of General Systems Theory (GST).

This paper provides an answer to the epistemological problem of how to perceive the substance of the IC domain. The derived ontology for the IC domain recognises different IC ontological dimensions that correspond to the IC ontological assumptions of different IC research streams, attempts to associate the IC of an organization with the tangible dimension of the enterprise, and provides conceptual semantics for modelling firm‐specific IC domains under the prism of different epistemological perspectives for IC.

The proposed ontological proposition for the IC domain is an abstract ontology. It might empower researchers with guidelines for systemising the formulation of theoretical propositions and descriptions for their research fields. Yet, as an abstract ontology, it might create difficulty in its practical implementation.

The ontological proposition for the IC domain can contribute towards the debate on the establishment of a common research communicational rationalism within the IC research community for coordinating individual research efforts.

Despite the common understanding that business model (BM) innovation is of vital importance for securing competitive positioning in the market place, managers still seem to lack appropriate frameworks and tools which can support them in renewing and rejuvenating their company’s existing BM. The purpose of this paper is to develop a structural and comprehensive toolbox of available BM configurations, from which companies can choose, to innovate their BM upon, and to design an appropriate BM innovation framework which can facilitate them in re-designing, selecting, and implementing new BM configuration possibilities.

A structured literature review is conducted to identify all the relevant BM configurations. Then, a value driver analysis is performed to group these BM configurations into appropriate categories. Finally, an ontological classification scheme and a structural and workable process, i.e. a BM innovation framework, are inductively developed.

The paper systematically develops a list of 71 BM configurations and groups them into an ontological classification scheme according to five groups: Value Proposition, Value Segment, Value Configuration, Value Network, and Value Capture. The paper illustrates how the BM innovation framework, enabled by this ontological classification scheme, provides a platform for identifying BM innovation routes for companies, allowing managers to envisage radical, disruptive, and new-to-the-world BM configuration ideas, or apply existing configurations from other industrial settings in what may be deemed new-to-the-industry innovation.

The paper enriches the amount of potential BM configurations available for managers to choose from when innovating their BMs, and extends the analysis to five core BM configuration categories. Moreover, the BM innovation framework suggested highlights the strong relationships among the value drivers, thus presenting the opportunity for managers to assess potential conflicts or synergies between various value drivers, and to align the BM management process as a whole.

The purpose of this article is to evaluate the development and use of the gene ontology (GO), a scientific vocabulary widely used in molecular biology databases, with particular reference to the relation between the theoretical basis of the GO, and the pragmatics of its application.

The study uses a combination of bibliometric analysis, content analysis and discourse analysis. These analyses focus on details of the ways in which the terms of the ontology are amended and deleted, and in which they are applied by users.

Although the GO is explicitly based on an objective realist epistemology, a considerable extent of subjectivity and social factors are evident in its development and use. It is concluded that bio-ontologies could beneficially be extended to be pluralist, while remaining objective, taking a view of concepts closer to that of more traditional controlled vocabularies.

This is one of very few studies which evaluate the development of a formal ontology in relation to its conceptual foundations, and the first to consider the GO in this way.

The Reflective Middleware for Acoustic Management (ReM-AM), based on the Middleware for Cloud Learning Environments (AmICL), aims to improve the interaction between users and agents in a Smart Environment (SE) using acoustic services, in order to consider the unpredictable situations due to the sounds and vibrations. The middleware allows observing, analyzing, modifying and interacting in every state of a SE from the acoustics. This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management.

This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. In this paper are defined the different domains of knowledge required for the management of the sounds in SEs, which are modeled using ontologies.

This work proposes an acoustics and sound ontology, a service-oriented architecture (SOA) ontology, and a data analytics and autonomic computing ontology, which work together. Finally, the paper presents three case studies in the context of smart workplace (SWP), ambient-assisted living (AAL) and Smart Cities (SC).

Future works will be based on the development of algorithms for classification and analysis of sound events, to help with emotion recognition not only from speech but also from random and separate sound events. Also, other works will be about the definition of the implementation requirements, and the definition of the real context modeling requirements to develop a real prototype.

In the case studies is possible to observe the flexibility that the ReM-AM middleware based on the ODA paradigm has by being aware of different contexts and acquire information of each, using this information to adapt itself to the environment and improve it using the autonomic cycles. To achieve this, the middleware integrates the classes and relations in its ontologies naturally in the autonomic cycles.

The main contribution of this work is the description of the ontologies required for future works about acoustic management in SE, considering that what has been studied by other works is the utilization of ontologies for sound event recognition but not have been expanded like knowledge source in an SE middleware. Specifically, this paper presents the theoretical framework of this work composed of the AmICL middleware, ReM-AM middleware and the ODA paradigm.

Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications.

The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing an analysis of the widely used Basic Formal Ontology (BFO) through digital methods and visualizations, and presents historical research alongside unstructured interview data with leading experts in BFO development.

The author found that ACOs are products of communal deliberation and decision making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data.

The paper presents a new approach for studying ACOs, the social impact of ACO work, and describes methods that may be used to produce further applied ontology studies.

Classification is an important process in making sense of the world, and has a pronounced social dimension. This paper aims to compare folksonomy, a new social classification system currently being developed on the web, with conventional taxonomy in the light of theoretical sociological and anthropological approaches. The co‐existence of these two types of classification system raises the questions: Will and should taxonomies be hybridized with folksonomies? What can each of these systems contribute to information‐searching processes, and how can the sociology of knowledge provide an answer to these questions? This paper aims also to address these issues.

This paper is situated at the meeting point of the sociology of knowledge, epistemology and information science and aims at examining systems of classification in the light of both classical theory and current late‐modern sociological and anthropological approaches.

Using theoretical approaches current in the sociology of science and knowledge, the paper envisages two divergent possible outcomes.

While concentrating on classifications systems, this paper addresses the more general social issue of what we know and how it is known. The concept of hybrid knowledge is suggested in order to illuminate the epistemological basis of late‐modern knowledge being constructed by hybridizing contradictory modern knowledge categories, such as the subjective with the objective and the social with the natural. Integrating tree‐like taxonomies with folksonomies or, in other words, generating a naturalized structural order of objective relations with social, subjective classification systems, can create a vast range of hybrid knowledge.

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.