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The broader adoption of the internet along with web‐based systems has defined a new way of exchanging information. That advance added by the multiplication of mobile devices has required systems to be even more flexible and personalized. Maybe because of that, the traditional teaching‐controlled learning style has given up space to a new way of learning, which is more flexible and adequate to the learners needs. The purpose of this research is to go further into the semantic modeling of adaptive web based learning systems. Particularly, the paper focuses on those learning systems that consider in their definition the awareness of student's context in order to properly react to the student needs.

In this paper the authors introduce a semantic model of the student context in terms of an ontology network. This semantic model is explored in order to detect the “current situation” of students when they are navigating into e‐learning environments. The final objective is to enrich the adaptation functionality of e‐learning environments, being able to evaluate context data from personal profile, learning domain and technological situation.

In order to evaluate the semantic model defined, examples of detected situations are shown in accordance to specific e‐learning scenarios.

The paper covers definition of a flexible and modularized model by using ontology networks, which can be easily modified to incorporate new knowledge data, aiding the modeling of concepts from different learning environments.

Web site recommendation systems help to get high quality information. The modelling of recommendation systems involves the combination of many features: metrics of quality, quality criteria, recommendation criteria, user profile, and specific domain concepts, among others. At the moment of the specification of a recommendation system it must be guaranteed a right interrelation of all of these features. The purpose of this paper is to model a web site quality‐based recommendation system by an ontology network.

In this paper, the authors propose an ontology network based process for web site recommendation modelling. The ontology network conceptualizes the different domains (web site domain, quality assurance domain, user context domain, recommendation criteria domain, specific domain) in a set of interrelated ontologies. Particularly, this approach is illustrated for the health domain.

Basically, this work introduces the semantic relationships that were used to construct this ontology network. Moreover, it shows the usefulness of this ontology network for the detection of possible inconsistencies when specifying recommendation criteria.

Recommendation systems based on ontologies that model the user profile and the domain of resources to be recommended are quite common. However, it is uncommon to find models that explicitly represent the criteria used by the recommender systems, that express the quality dimensions of resources and on which criteria are applied, and consider the user context at the moment of the query.

The purpose of this paper is to highlight the ontological implications of combining network and system ontology to conceptualize industrial networks as the empirical manifestations of complex adaptive economic systems.

This paper contributes with a systematic discussion on how network and system ontology can be combined to produce better understandings of business networks. It also provides a review of the state-of-the art research literature on the topic as a starting point for the discussion.

Findings indicate that networks may be enclosed in each other constituting sub- and supra-networks comprising increasing complexity. In these cases, sub-networks that are black-boxed can be seen as entities in themselves producing inputs and outputs to the supra-network. Networks, once they become black-boxed, can assume the functions of generative mechanisms within a wider supra-network.

This research is conceptual in nature and needs to be complemented with empirical research. In addition, the literature review used one database complemented with papers from the IMP journal. A wider search could reveal additional research that can be of relevance for the development of the field.

This paper addresses the ontological and methodological issues arising from a mixed system and network ontology. These issues are commonly ignored or dealt with indirectly in extant literature. For any accumulation of knowledge in the field to be possible, the explication of a mixed ontology is important as it have conceptual and methodological consequences. Adopting such a mixed ontological position provides an ontology in line with empirical research of business practice.

The development of adaptable and intelligent educational systems is widely considered one of the great challenges in scientific research. Among key elements for building advanced training systems, an important role is played by methodologies chosen for knowledge representation. In this scenario, the introduction of ontology formalism can improve the quality of formative process, allowing the introduction of new and effective services. Ontology can lead to important improvements in the definition of courses knowledge domain, in the generation of adapted learning path and in the assessment phase. The purpose of this paper is to provide an initial discussion of the role of ontology in the context of e‐learning. It seeks to discuss the improvements related to the introduction of ontology formalism in the e‐learning field and to show a novel algorithm for ontology building through the use of Bayesian networks. Finally, it aims to illustrate its application in the assessment process and some experimental results.

A novel method for learning ontology for e‐learning is illustrated, using an approach based on Bayesian networks. Thanks to their characteristics, these networks can be used to model and evaluate the conditional dependencies among the nodes of ontology on the basis of the data obtained from student tests. An experimental evaluation of the proposed method was performed using real student data.

The proposed method was integrated in a tool for the assessment of students during a learning process. This tool is based on the use of ontology and Bayesian network. In particular through the matching between ontology and Bayesian network, it was found that our tool allows an effective tutoring and a better adaptation of learning process to demands of students. The assessment based on Bayesian approach allows a deeper analysis of student's knowledge.

The proposed approach needs more experimentation with other domains and with more complex ontology.

This paper provides an initial discussion of the role of ontology in the context of e‐learning. The improvements related to the introduction of ontology formalism in the e‐learning field are discussed and a novel algorithm for ontology building through the use of Bayesian Networks is showed. Finally, its application in the assessment process and some experimental results are illustrated.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

Current networks are providing plenty of services that users can access and use. These services are more and more pervasive and deployed in different networks distributed across an environment. This raises the problem of managing such environments in order to grant access to services from anywhere and to adapt the environment’s networks to dynamic changes. Also, there is a need of an autonomous behavior to reduce human intervention and assure environment’s consistency. This autonomous and distributed behavior leads to the definition and integration of existing and new technologies to enable autonomous distributed management. This is fulfilled by providing paradigms that bring awareness about the surroundings and enabled tools to manage and adapt the environment’s resources. The main problem is to dynamically provide auto‐configuration of networks to deal with the frequent changes which results from users’ roaming, changing services constraints, and changing services themselves, and adding, upgrading or removing policies. The outcome of these issues is a dynamic system with complex management. Hence, this paper proposes the integration of different techniques to provide an autonomous, distributed, and secure management including auto‐configuration, adaptation, and auto‐protection of pervasive environments. Then, policies control the behavior of the environment, devices configuration and the enforcement of security mechanisms to protect sensitive data. Also, mobile agents are employed to distribute management tasks across the distributed environment. In order to provide auto‐protection capabilities, the autonomous behavior of the environment have to be secured. Actually, this security issue is addressed by defining an agent‐based Public Key Infrastructure (PKI) with X.509 certificates. The agent ensures then that the security functions are applied across all the distributed networks, where specific agents are responsible for conveying necessary information and certificates to local environments. Finally, the paper proposes a semantic‐based privacy management approach using ontologies to decide how privacy information is handled in the environment.

Supply chain risks (SCRs) do not work in isolation and have impact both on each member of a chain and the performance of the entire supply chain. The purpose of this paper is to quantitatively assess the impact of dynamic risk propagation within and between integrated firms in global fresh produce supply chains.

A risk propagation ontology-based Bayesian network (BN) model was developed to measure dynamic SCR propagation. The proposed model was applied to a two-tier Australia-China table grape supply chain (ACTGSC) featured with an upstream Australian integrated grower and exporter and a downstream Chinese integrated importer and online retailer.

An ontology-based BN can be generated to accurately represent the risk domain of interest using the knowledge and inference capabilities inherent in a risk propagation ontology. In addition, the analyses revealed that supply discontinuity, product inconsistency and/or delivery delay originating in the upstream firm can propagate to increase the downstream firm’s customer value risk and business performance risk.

The work was conducted in an Australian-China table grape supply chain, so results are only product chain-specific in nature. Additionally, only two state values were considered for all nodes in the model, and finally, while the proposed methodology does provide a large-scale risk network map, it may not be appropriate for a large supply chain network as it only follows the process flow of a single supply chain.

This study supports the backward-looking traceability of risk root causes through the ACTGSC and the forward-looking prediction of risk propagation to key risk performance measures.

The methodology used in this paper provides an evidence-based decision-making capability as part of a system-wide risk management approach and fosters collaborative SCR management, which can yield numerous societal benefits.

The proposed methodology addresses the challenges in using a knowledge-based approach to develop a BN model, particularly with a large-scale model and integrates risk and performance for a holistic risk propagation assessment. The combination of modelling approaches to address the issue is unique.

Assigning business process activities to agents (human or automated) for their performance or supervision is a critical issue in business process management. Role‐based approaches are commonly used to specify work assignment policies, with roles defined as collections of capabilities and privileges required to perform job functions. The purpose of this paper is to address the activity assignment problem through a competency‐based approach. In this context, an ontology‐based competency model is developed to assist in identifying the competencies that exist in an organization and the competencies required, by workflow activities and in performing a competency gap analysis as a prerequisite for domain‐specific user development through competency‐based training.

An approach for developing a business process activity assignment policy based on an ontology‐based competency model is presented. This model is also used to define domain‐specific training courses that enable users meet the competency requirements of process activities. In broad terms, the approach consists of the following steps: identification of the competencies required in order to perform the various activities involved in each business process and definition of roles based on these competencies; identification of the competencies acquired in the organization and assignment of users to roles; performance of competency gap analysis to identify the missing user competencies for role playing and identification of user development needs; and development of competency‐based training scenarios intended to fill the user competency gaps.

An experimental implementation of the ontology‐based competency model proposed in the banking domain provided a fine‐grained role structure that was based on the competencies required by business process activities, and a user‐to‐role assignment that closely matched the competencies required for role playing, and brought forward missing user competencies that pointed to required user training needs.

The proposed ontology‐based competency model fulfils the need for a sustained work assignment approach based on user roles. To this end, roles and users are defined as collections of required and acquired competencies, respectively. A novel approach based on ontology‐based competency ontologies was also developed to fill required but missing user competencies.

To demonstrate that newer developments in the semantic web community, particularly those based on ontologies (simple knowledge organization system and others) mitigate common arguments from the digital library (DL) community against participation in the Semantic web.

The approach is a semantic web discussion focusing on the weak structure of the Web and the lack of consideration given to the semantic content during indexing.

The points criticised by the semantic web and ontology approaches are the same as those of the DL “Shell model approach” from the mid‐1990s, with emphasis on the centrality of its heterogeneity components (used, for example, in vascoda). The Shell model argument began with the “invisible web”, necessitating the restructuring of DL approaches. The conclusion is that both approaches fit well together and that the Shell model, with its semantic heterogeneity components, can be reformulated on the semantic web basis.

A reinterpretation of the DL approaches of semantic heterogeneity and adapting to standards and tools supported by the W3C should be the best solution. It is therefore recommended that – although most of the semantic web standards are not technologically refined for commercial applications at present – all individual DL developments should be checked for their adaptability to the W3C standards of the semantic web.

A unique conceptual analysis of the parallel developments emanating from the digital library and semantic web communities.

This paper aims to take a new look at how scenarios are produced and used. It does so from a perspective that is unusual in the field: network pragmatism.

This paper takes a conceptual approach.

A network pragmatist account allows scenarios to play an important role in actions designed to secure specific futures for organisations. It, thus, endows them with micro-political force. Any scenario that fails to exert this force will wither and, ultimately, die, but it can be resuscitated. With its demise in the networked world, a scenario can assume a more partial and private existence, shaping the affections, loyalties and actions of notable individuals.

This approach generates novel propositions that question the adequacy of currently dominant cognitive theories. However, it has yet to be tested empirically.

Pragmatist reading of scenarios that is proposed is not only distinct but also only ever partial. This work emphasises that a holistic account of scenario lives needs multiple theoretical perspectives.