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The common understanding of generalization/specialization relations assumes the relation to be equally strong between a classifier and any of its related classifiers and also at every level of the hierarchy. Assigning a grade of relative distance to represent the level of similarity between the related pairs of classifiers could correct this situation, which has been considered as an oversimplification of the psychological account of the real-world relations. The paper aims to discuss these issues.

The evaluation followed an end-user perspective. In order to obtain a consistent data set of specialization distances, a group of 21 persons was asked to assign values to a set of relations from a selection of terms from the AGROVOC thesaurus. Then two sets of representations of the relations between the terms were built, one according to the calculated concept of specialization weights and the other one following the original order of the thesaurus. In total, 40 persons were asked to choose between the two sets following an A/B test-like experiment. Finally, short interviews were carried out after the test to inquiry about their decisions.

The results show that the use of this information could be a valuable tool for search and information retrieval purposes and for the visual representation of knowledge organization systems (KOS). Furthermore, the methodology followed in the study turned out to be useful for detecting inconsistencies in the thesaurus and could thus be used for quality control and optimization of the hierarchical relations.

The use of this relative distance information, namely, “concept specialization distance,” has been proposed mainly at a theoretical level. In the current experiment, the authors evaluate the potential use of this information from an end-user perspective, not only for text-based interfaces but also its application for the visual representation of KOS. Finally, the methodology followed for the elaboration of the concept specialization distance data set showed potential for detecting possible inconsistencies in KOS.

The purpose of this Guest Editorial is to introduce the papers in this special issue.

A brief summary of the main contributions of the papers included in this issue is provided.

The idea of this special issue was an outcome of the organization of activities of the First Online Conference on Metadata and Semantics Research (MTSR'05).

The emphasis on “semantics” addresses the importance of the languages in which metadata records are created, their adequacy for the functions to be enabled and their modular design to adapt to present and future requirements.

The purpose of this paper is to employ the case of Organization for Economic Cooperation and Development (OECD) data repositories to examine the potential of blockchain technology in the context of addressing basic contemporary societal concerns, such as transparency, accountability and trust in the policymaking process. Current approaches to sharing data employ standardized metadata, in which the provider of the service is assumed to be a trusted party. However, derived data, analytic processes or links from policies, are in many cases not shared in the same form, thus breaking the provenance trace and making the repetition of analysis conducted in the past difficult. Similarly, it becomes tricky to test whether certain conditions justifying policies implemented still apply. A higher level of reuse would require a decentralized approach to sharing both data and analytic scripts and software. This could be supported by a combination of blockchain and decentralized file system technology.

The findings presented in this paper have been derived from an analysis of a case study, i.e., analytics using data made available by the OECD. The set of data the OECD provides is vast and is used broadly. The argument is structured as follows. First, current issues and topics shaping the debate on blockchain are outlined. Then, a redefinition of the main artifacts on which some simple or convoluted analytic results are based is revised for some concrete purposes. The requirements on provenance, trust and repeatability are discussed with regards to the architecture proposed, and a proof of concept using smart contracts is used for reasoning on relevant scenarios.

A combination of decentralized file systems and an open blockchain such as Ethereum supporting smart contracts can ascertain that the set of artifacts used for the analytics is shared. This enables the sequence underlying the successive stages of research and/or policymaking to be preserved. This suggests that, in turn, and ex post, it becomes possible to test whether evidence supporting certain findings and/or policy decisions still hold. Moreover, unlike traditional databases, blockchain technology makes it possible that immutable records can be stored. This means that the artifacts can be used for further exploitation or repetition of results. In practical terms, the use of blockchain technology creates the opportunity to enhance the evidence-based approach to policy design and policy recommendations that the OECD fosters. That is, it might enable the stakeholders not only to use the data available in the OECD repositories but also to assess corrections to a given policy strategy or modify its scope.

Blockchains and related technologies are still maturing, and several questions related to their use and potential remain underexplored. Several issues require particular consideration in future research, including anonymity, scalability and stability of the data repository. This research took as example OECD data repositories, precisely to make the point that more research and more dialogue between the research and policymaking community is needed to embrace the challenges and opportunities blockchain technology generates. Several questions that this research prompts have not been addressed. For instance, the question of how the sharing economy concept for the specifics of the case could be employed in the context of blockchain has not been dealt with.

The practical implications of the research presented here can be summarized in two ways. On the one hand, by suggesting how a combination of decentralized file systems and an open blockchain, such as Ethereum supporting smart contracts, can ascertain that artifacts are shared, this paper paves the way toward a discussion on how to make this approach and solution reality. The approach and architecture proposed in this paper would provide a way to increase the scope of the reuse of statistical data and results and thus would improve the effectiveness of decision making as well as the transparency of the evidence supporting policy.

Decentralizing analytic artifacts will add to existing open data practices an additional layer of benefits for different actors, including but not limited to policymakers, journalists, analysts and/or researchers without the need to establish centrally managed institutions. Moreover, due to the degree of decentralization and absence of a single-entry point, the vulnerability of data repositories to cyberthreats might be reduced. Simultaneously, by ensuring that artifacts derived from data based in those distributed depositories are made immutable therein, full reproducibility of conclusions concerning the data is possible. In the field of data-driven policymaking processes, it might allow policymakers to devise more accurate ways of addressing pressing issues and challenges.

This paper offers the first blueprint of a form of sharing that complements open data practices with the decentralized approach of blockchain and decentralized file systems. The case of OECD data repositories is used to highlight that while data storing is important, the real added value of blockchain technology rests in the possible change on how we use the data and data sets in the repositories. It would eventually enable a more transparent and actionable approach to linking policy up with the supporting evidence. From a different angle, throughout the paper the case is made that rather than simply data, artifacts from conducted analyses should be made persistent in a blockchain. What is at stake is the full reproducibility of conclusions based on a given set of data, coupled with the possibility of ex post testing the validity of the assumptions and evidence underlying those conclusions.

This paper reports on a quantitative study of data gathered from the Linked Open Vocabularies (LOV) catalogue, including the use of network analysis and metrics. The purpose of this paper is to gain insights into the structure of LOV and the use of vocabularies in the Web of Data. It is important to note that not all the vocabularies in it are registered in LOV. Given the de-centralised and collaborative nature of the use and adoption of these vocabularies, the results of the study can be used to identify emergent important vocabularies that are shaping the Web of Data.

The methodology is based on an analytical approach to a data set that captures a complete snapshot of the LOV catalogue dated April 2014. An initial analysis of the data is presented in order to obtain insights into the characteristics of the vocabularies found in LOV. This is followed by an analysis of the use of Vocabulary of a Friend properties that describe relations among vocabularies. Finally, the study is complemented with an analysis of the usage of the different vocabularies, and concludes by proposing a number of metrics.

The most relevant insight is that unsurprisingly the vocabularies with more presence are those used to model Semantic Web data, such as Resource Description Framework, RDF Schema and OWL, as well as broadly used standards as Simple Knowledge Organization System, DCTERMS and DCE. It was also discovered that the most used language is English and the vocabularies are not considered to be highly specialised in a field. Also, there is not a dominant scope of the vocabularies. Regarding the structural analysis, it is concluded that LOV is a heterogeneous network.

The paper provides an empirical analysis of the structure of LOV and the relations between its vocabularies, together with some metrics that may be of help to determine the important vocabularies from a practical perspective. The results are of interest for a better understanding of the evolution and dynamics of the Web of Data, and for applications that attempt to retrieve data in the Linked Data Cloud. These applications can benefit from the insights into the important vocabularies to be supported and the value added when mapping between and using the vocabularies.

The purpose of this paper is to provide a model for a process‐oriented view on learning in organizations, and to link this model with IMS Learning Design (LD), a language for the description of pedagogical arrangement of multi‐role activities.

This article exploits conceptual modeling techniques and a literature review.

A tentative mapping of the GOAP model to LD constructs is sketched, and some tentative aspects that suggest the need for an extended specification embedding LD are discussed.

This paper describes a model for a process‐oriented view on learning in organizations, and sketches how that framework could be integrated with IMS Learning Design, a language for the description of pedagogical arrangement of multi‐role activities.

The paper promotes the role of conceptual modeling as a key process for learning design.

The paper presents an exploitation of learning processes modeling towards effective learning designs.

The aim of this paper is introducing the concept of a “semantic learning organization” (SLO) as an extension of the concept of “learning organization” in the technological domain.

The paper takes existing definitions and conceptualizations of both learning organizations and Semantic Web technology to develop the new concept.

The main points in which Semantic Web technology can be applied to learning in organizations are identified, and ontological accounts of organizational earning behaviour are pointed out as the main open question to develop the concept of a SLO.

The paper provides a new conceptual framework for Semantic Web applications in organizational learning, which can be used as a roadmap for further research.

The aim of this paper is to introduce the special issue on the Semantic Web. Intensive research has been undertaken worldwide in research centres and several achievements have been accomplished towards the ultimate objective: the expression and the exploitation of humanity's collective knowledge.

A multi‐fold strategy for the preparation of this special issue was deployed. Given the special characteristics, a balanced mix of introductory papers to the topic, and also advanced research papers at the leading edge of Semantic Web evolution, were selected.

The paper summarizes the articles of the issue and also provides help in understanding the Semantic Web Roadmap of Digital Libraries.

Introduces the papers in the special issue.

The aim of this paper is discussing about the similarities between the life cycle of knowledge management and the processes in which learning objects are created, evaluated and used.

The paper describes LO and learning designs and depicts their integration into the knowledge life cycle (KLC) of the KMCI, showing the main ontological definitions required to represent the proposed integration, putting them into relation with previous research in the topic.

The main points in that the “learning object” concept can be connected to existing KLC models in order to provide a more comprehensive framework for reuse‐oriented e‐learning and knowledge management.

The paper describes how to integrate learning‐oriented artefacts as part of the organizational context, in particular, as part of the knowledge life cycle (KLC). It also depicts the integration of a reuse‐oriented e‐learning framework into the KLC of the KMCI in the form of ontological definitions.