Federated semantic search using terminological thesauri for learning object discovery

Abstract

Purpose 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). Design/methodology/approach 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). Findings 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. Practical implications 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. Originality/value 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.