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The purpose of this paper is to focus on the quality of the connections (linkset) among thesauri published as Linked Data on the Web. It extends the cross-walking measures with two new measures able to evaluate the enrichment brought by the information reached through the linkset (lexical enrichment, browsing space enrichment). It fosters the adoption of cross-walking linkset quality measures besides the well-known and deployed cardinality-based measures (linkset cardinality and linkset coverage).

The paper applies the linkset measures to the Linked Thesaurus fRamework for Environment (LusTRE). LusTRE is selected as testbed as it is encoded using a Simple Knowledge Organisation System (SKOS) published as Linked Data, and it explicitly exploits the cross-walking measures on its validated linksets.

The application on LusTRE offers an insight of the complementarities among the considered linkset measures. In particular, it shows that the cross-walking measures deepen the cardinality-based measures analysing quality facets that were not previously considered. The actual value of LusTRE’s linksets regarding the improvement of multilingualism and concept spaces is assessed.

The paper considers skos:exactMatch linksets, which belong to a rather specific but a quite common kind of linkset. The cross-walking measures explicitly assume correctness and completeness of linksets. Third party approaches and tools can help to meet the above assumptions.

This paper fulfils an identified need to study the quality of linksets. Several approaches formalise and evaluate Linked Data quality focusing on data set quality but disregarding the other essential component: the connection among data.

The Integrative Levels Classification (ILC) is a comprehensive “freely faceted” knowledge organization system not previously expressed as SKOS (Simple Knowledge Organization System). This paper reports and reflects on work converting the ILC to SKOS representation.

The design of the ILC representation and the various steps in the conversion to SKOS are described and located within the context of previous work considering the representation of complex classification schemes in SKOS. Various issues and trade-offs emerging from the conversion are discussed. The conversion implementation employed the STELETO transformation tool.

The ILC conversion captures some of the ILC facet structure by a limited extension beyond the SKOS standard. SPARQL examples illustrate how this extension could be used to create faceted, compound descriptors when indexing or cataloguing. Basic query patterns are provided that might underpin search systems. Possible routes for reducing complexity are discussed.

Complex classification schemes, such as the ILC, have features which are not straight forward to represent in SKOS and which extend beyond the functionality of the SKOS standard. The ILC's facet indicators are modelled as rdf:Property sub-hierarchies that accompany the SKOS RDF statements. The ILC's top-level fundamental facet relationships are modelled by extensions of the associative relationship – specialised sub-properties of skos:related. An approach for representing faceted compound descriptions in ILC and other faceted classification schemes is proposed.

The purpose of this research is to introduce a mapping of the Simple Knowledge Organization Systems (SKOS) Core metadata to an ontology‐based model, whose main aim is to foster the semantic interoperability of different concept schemes.

Research objectives have been achieved through the introduction of a common ground for the definition of concepts, based on shared definitions included in widely used upper ontologies. This effort makes use of a particular upper ontology: OpenCyc, the open source version of Cyc, which is currently one of the most complete general knowledge bases.

An in‐depth study of the SKOS vocabulary has suggested its extension, with the aim of correcting the shortcomings related with SKOS schemes semantic interoperability. However, although such an extension would help avoid ambiguities and enable inter‐thesaurus interoperability, the paper is focused on using a non‐invasive contribution. Non‐invasive in that the SKOS Core should not be modified as a result of this activity, but also, non‐invasive in that current SKOS schemes would not require modifications.

The use of formal representations to provide the SKOS terms with computational semantics, as well as the introduction of an intermediate ontology‐based model built on the SKOS information. Although both proposals stand on one upper ontology (OpenCyc), they could be easily adapted to others, which provides an added value to this research work.

New technologies allow for efficient dissemination of scientific knowledge objects (SKOs). Yet they are likely to transform SKOs as well. The aim of this paper is to propose a way to structure SKOs that allows for both a clear individuation of the main scientific contributions and a fine‐grained structure of credits and evaluation.

The authors review and analyze existing practices of structuring SKOs in different disciplines.

Provisionally considering the published paper as an atomic SKO, possible subatomic structures of SKOs are investigated. It is hypothesized that SKOs are meant to satisfy two separated but interdependent sets of constraints, one related to the contribution the SKO makes to the body of knowledge, and another related to the contribution the SKO makes to the reputation of its authors. It is hypothesized that existing SKO structures are not optimal for satisfying both sets of constraints at once.

A broader analysis may be needed that covers the totality of existing practices.

Guidelines are offered. This paper, including the present abstract, is an example of what the scientific paper of tomorrow could be like.

The paper proposes better apportioning of scientific credits and evaluation; substantive evolution of the academic publishing and credit attribution models.

The idea that the communication and evaluation function of a SKO are differently reflected in their structure is novel. The proposed fine‐grained credit attribution system is novel. The molecular/atomic/sub‐atomic distinction is a new way to fix the terminology.

The purpose of this paper is to propose a methodology for assessing thesauri and other controlled vocabularies management tools that can represent content using the Simple Knowledge Organization System (SKOS) data model, and their use in a Linked Open Data (LOD) paradigm. It effectively analyses selected set of tools in order to prove the validity of the method.

A set of 27 criteria grouped in five evaluation indicators is proposed and applied to ten vocabulary management applications which are compliant with the SKOS data model. Previous studies of controlled vocabulary management software are gathered and analyzed, to compare the evaluation parameters used and the results obtained for each tool.

The results indicate that the tool that obtains the highest score in every indicator is Poolparty. The second and third tools are, respectively, TemaTres and Intelligent Theme Manager, but scoring lower in most of the evaluation items. The use of a broad set of criteria to evaluate vocabularies management tools gives satisfactory results. The set of five indicators and 27 criteria proposed here represents a useful evaluation system in the selection of current and future tools to manage vocabularies.

The paper only assesses the ten most important/well know software tools applied for thesaurus and vocabulary management until October 2016. However, the evaluation criteria could be applied to new software that could appear in the future to create/manage SKOS vocabularies in compliance with LOD standards.

The originality of this paper relies on the proposed indicators and criteria to evaluate vocabulary management tools. Those criteria and indicators can be valuable also for future software that might appear. The indicators are also applied to the most exhaustive and qualified list of this kind of tools. The paper will help designers, information architects, metadata librarians, and other staff involved in the design of digital information systems, to choose the right tool to manage their vocabularies in a LOD/vocabulary scenario.

The purpose of this paper is to introduce digital librarians to a new encoding standard for developing semantically‐rich controlled vocabularies which will enhance searching of digital content.

Literature‐based review.

SKOS Core serves as an efficient and practical segue from traditional knowledge organization systems such as thesauri and classification systems to more complex and logic‐driven Semantic Web languages.

Librarians with a need to provide semantically‐rich access to digital resources will find SKOS Core a useful addition to their metadata tool kits.

Provides a useful and practical introduction to a new Semantic Web tool which should be of great interest to librarians tasked with integrating knowledge organization systems into digital libraries.

Effective synthesis of learning material is a multidimensional problem, which often relies on handpicking approaches and human expertise. Sources of educational content exist in a variety of forms, each offering proprietary metadata information and search facilities. This paper aims to show that it is possible to harvest scholarly resources from various repositories of open educational resources (OERs) in a federated manner. In addition, their subject can be automatically annotated using ontology inference and standard thematic terminologies.

Based on a semantic interpretation of their metadata, authors can align external collections and maintain them in a shared knowledge pool known as the Learning Object Ontology Repository (LOOR). The author leverages the LOOR and show that it is possible to search through various educational repositories’ metadata and amalgamate their semantics into a common learning object (LO) ontology. The author then proceeds with automatic subject classification of LOs using keyword expansion and referencing standard taxonomic vocabularies for thematic classification, expressed in SKOS.

The approach for automatic subject classification simply takes advantage of the implicit information in the searching and selection process and combines them with expert knowledge in the domain of reference (SKOS thesauri). This is shown to improve recall by a considerable factor, while precision remains unaffected.

To the best of the author’s knowledge, the idea of subject classification of LOs through the reuse of search query terms combined with SKOS-based matching and expansion has not been investigated before in a federated scholarly setting.

The purpose of this paper is to propose a framework and system to address the inability to discover new and authentic learning material and the lack of a single access point for search and browsing of remote learning object repositories (LORs).

The authors develop a framework for keyword-based query expansion using SKOS domain terminologies and implement a federated search mechanism integrating various disparate LORs within a learning management system (LMS).

The authors show that the expanded query achieves improved information gain and it is applied for federated information access, by simultaneously searching within a number of repositories. Results can be seamlessly aggregated back within the LMS and the course context.

It is possible to retrieve additional learning objects (LOs) and achieve a corresponding increase in recall, while maintaining precision. SKOS expansion behaves well in a scholarly setting, which, combined with federated search, can contribute toward LOs’ discovery at a balanced cost. The system can be easily integrated with other platforms as well, building on open standards and RESTful communication.

To the authors’ knowledge, this is the first time SKOS-based query expansion is applied in a federated setting, and for the discovery and alignment of learning objects residing within LORs. The results show that this approach can achieve considerable information gain and that it is possible to strike a balance between search effectiveness, query drift and performance.

To show how semantic web techniques can help address semantic interoperability issues in the broad cultural heritage domain, allowing users an integrated and seamless access to heterogeneous collections.

This paper presents the heterogeneity problems to be solved. It introduces semantic web techniques that can help in solving them, focusing on the representation of controlled vocabularies and their semantic alignment. It gives pointers to some previous projects and experiments that have tried to address the problems discussed.

Semantic web research provides practical technical and methodological approaches to tackle the different issues. Two contributions of interest are the simple knowledge organisation system model and automatic vocabulary alignment methods and tools. These contributions were demonstrated to be usable for enabling semantic search and navigation across collections.

The research aims at designing different representation and alignment methods for solving interoperability problems in the context of controlled subject vocabularies. Given the variety and technical richness of current research in the semantic web field, it is impossible to provide an in‐depth account or an exhaustive list of references. Every aspect of the paper is, however, given one or several pointers for further reading.

This article provides a general and practical introduction to relevant semantic web techniques. It is of specific value for the practitioners in the cultural heritage and digital library domains who are interested in applying these methods in practice.

This paper aims to propose an entity-based scientific metadata schema, i.e. Scientific Knowledge Object (SKO) Types. During the past 50 years, many metadata schemas have been developed in a variety of disciplines. However, current scientific metadata schemas focus on describing data, but not entities. They are descriptive, but few of them are structural and administrative.

To describe entities in scientific knowledge, the theory of SKO Types is proposed. SKO Types is an entity-based theory for representing and linking SKOs. It defines entities, relationships between entities and attributes of each entity in the scientific domain.

In scientific knowledge management, SKO Types serves as the basis for relating entities, entity components, aggregated entities, relationships and attributes to various tasks, e.g. linked entity, rhetorical structuring, strategic reading, semantic annotating, etc., that users may perform when consulting ubiquitous SKOs.

SKO Types can be widely applied in various digital libraries and scientific knowledge management systems, while for the existing legacy of scientific publications and their associated metadata schemas.