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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.

Successful learning infrastructures and repositories often depend on well-organized content collections for effective dissemination, maintenance and preservation of resources. By combining semantic descriptions already lying or implicit within their descriptive metadata, reasoning-based or semantic searching of these collections can be enabled and produce novel possibilities for content browsing and retrieval. The specifics and necessities of such an approach, however, make it hard to assess and measure its effectiveness. The paper aims to discuss these issues.

Therefore in this paper the authors introduce a concrete methodology toward a pragmatic evaluation of semantic searching in such scenarios, which is exemplified through the semantic search plugin the authors have developed for the popular DSpace repository system.

The results reveal that this approach can be appealing to expert as well as novice users alike, improve the effectiveness of content discovery and enable new retrieval possibilities in comparison to traditional, keyword-based search.

This paper presents applied research efforts to employ semantic searching techniques on digital repositories and to construct a novel methodology for evaluating the outcomes against various perspectives. Although this is original in itself, value lies also within the concrete and measurable results presented, accompanied by an analysis, that would be helpful to assess similar (i.e. semantic query answering and searching) techniques in the particular scenario of digital repositories and libraries and to evaluate corresponding investments. To the knowledge there has been hardly any other evaluation effort in the literature for this particular case; that is, to assess the merit and usage of advanced semantic technologies in digital repositories.

Major objective of the instant study was to investigate the factors affecting the adoption of integrated semantic digital libraries (SDLs). It attempted to find out the challenges faced in implementing semantic technologies in digital libraries. This study also aimed to develop a framework to provide practical solutions to efficiently adopt semantic digital library systems to offer richer data and services.

To meet the formulated objectives of the study, a systematic literature review was conducted. The authors adhered to the “Preferred Reporting Items for the Systematic Review and Meta-analysis” (PRISMA) guidelines as a research method. The data were retrieved from different tools and databases. In total, 35 key studies were included for systematic review after having applied standard procedures.

The findings of the study indicated that SDLs are highly significant as they offered context-based information resources. Interoperability of the systems, advancement in bilateral transfer modules, machine-controlled indexing, and folksonomy were key factors in developing semantic digital libraries. The study identified five different types of challenges to build an integrated semantic digital library system. These challenges included ontologies and interoperability, development of a suitable model, diversity in language, lack of skilled human resources, and other technical issues.

This paper provided a framework that is based on practical solutions as a benchmark for policymakers to devise formal standards for the initiation to develop integrated semantic digital libraries.