An empirical analysis of ontology-based query expansion for learning resource searches using MERLOT and the Gene ontology

Segura, N. Alejandra; Salvador-Sánchez,; García‐Barriocanal, Elena; Prieto, Manuel E.

doi:10.1016/j.knosys.2010.07.012

Cited by 34 publications

(7 citation statements)

References 39 publications

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“…The Ontology Query Expansion Algorithm [35] adapts an ontological approach to LO retrieval by expanding the query algorithm with semantic aggregation and a dynamic mechanism for searching and filtering LOs whose theme is genetics. This mechanism has been validated in the MERLOT (Multimedia Educational Resource for Learning and Online Teaching) repository where the mechanism searched for LOs using Gene Ontology [36]. The mechanism is connected to the MERLOT Restful service for the retrieval of contents.…”

Section: State Of the Artmentioning

confidence: 99%

Smart System for the Retrieval of Digital Educational Content

et al. 2019

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The education sector is a major generator, consumer, and depositary of educational content. Thanks to technological advances, today's educators and learners have ubiquitous and on-demand access to information. Technology has made it possible for us to communicate and share information effortlessly from anywhere in the world. However, the availability of vast amounts of heterogeneous educational content will not be useful unless we search, retrieve and integrate it, creating interoperable educational environments. The current challenges to integrating educational content arise from its distribution over several repositories. This research proposes AIREH (architecture for intelligent retrieval of educational content from heterogeneous environments), for the retrieval of digital content through agent-based virtual organizations. This flexible architecture facilitates the search for and integration of heterogeneous content through an information retrieval model that involves both case-based reasoning and federated search. Moreover, AIREH is based on an adaptive organization model for distributed planning, thanks to which, it manages open systems flexibly, dynamically, and effectively. The conducted case study gives very promising results and demonstrates the advantages of using agent-based virtual organizations in the retrieval of labeled digital content. The proposed model is flexible, customizable, comprehensive and efficient.In educational technology, one of the most widespread approaches to making educational content reusable and interoperable is to fragment it into independent, modular units that can be used in different environments and applications [1]. This modular approach makes it possible to easily access and combine educational content items for a single learning objective, this is termed as a learning object (LO) [2,3]. The ability to access simple didactic resources specifically designed for use in different contexts facilitates the process of creating educational content. This reduces the time of creating and compiling educational resources, enabling the efficient development of comprehensive educational courses. The change in the creation process takes place when it is based on the search and aggregation of these small modular pieces, as simple didactic resources specifically designed to be used in different contexts. Normally, LO are stored in digital repositories, called learning object repositories (LORs). These systems must also have the functionalities of importation, exportation, identification, and retrieval of content [4,5]. The user usually interacts with the information contained in the repository via their web browser.To support the reuse process (speed, flexibility, and uniformity), the learning object has external descriptors or labels, which enable the identification, organization, search and retrieval of content items. These descriptors are created with standard-based metadata, ensuring the interoperability of systems and the description of content items using an adequate set of attribu...

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Section: State Of the Artmentioning

confidence: 99%

Smart System for the Retrieval of Digital Educational Content

et al. 2019

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“…Thus, they expand the query used in the first search trial to refine the first search results. Query expansion improves the coverage (Segura, Salvador-Sánchez, García-Barriocanal, & Prieto, 2011) as researchers not only formulate queries using search keywords and conditions, but also expand them to improve search results. In this process, familiarity with the search topic affects the performance because a researcher who has knowledge about the topic can use appropriate synonyms for the search keywords (Sihvonen & Vakkari, 2004).…”

Section: Domain Knowledgementioning

confidence: 99%

Curriculum and instruction

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“…El poder expresivo de las consultas permite seleccionar los repositorios o los OA de acuerdo a consultas más exactas de los usuarios y, permite presentar los resultados cumpliendo mejor con sus expectativas. Varios trabajos [Lee et al, 2008], [Segura et al, 2010], [Segura et al, 2011] [Kärger et al, 2006 ponen de manifiesto el poco poder de expresividad en la recuperación de los OA en sus respectivos ROAs aportando propuestas de mejora tales como la incorporación de aspectos semánticos en la expansión de los mecanismos de consulta. [Shih et al, 2006] ha desarrollado una herramienta de edición de metadatos para apoyar la búsqueda y reutilización de OAs.…”

Section: Aproximaciones a La Recuperación Automática De Contenidos Ed...unclassified

“…El segundo de los trabajos [Segura et al, 2010] hace nuevas aportaciones al mecanismo de la expansión semántica de las consultas, pero se queda en una propuesta no contrastada sobre ROA reales y sobre sus archivos de metadatos etiquetados. La expansión de las consultas en [Segura et al, 2011] define un mecanismo dinámico de búsqueda y filtrado de los resultados que es validado en el repositorio MERLOT para la búsqueda de OA relacionados con el campo de la genética haciendo uso de la ontología Gene. El mecanismo de conexión con MERLOT utilizado está basado en un servicio Restful para la recuperación de los contenidos.…”

Section: Aproximaciones a La Recuperación Automática De Contenidos Ed...unclassified

“…The expressive power of queries to select LO repositories or queries according to user are more accurate and better present the results that meet user expectations. Various works [Lee et al, 2008], [Segura et al, 2010], [Segura et al, 2011] [Karger et al, 2006 show how a little power of expression in the recovery of LOs in their LORs can provide suggestions for improvement by incorporating many semantic aspects in the expansion of consultative mechanisms. [Shih et al, 2006] has developed a metadata authoring tool to support the search and reuse of LOs.…”

Section: Approaches To Automatic Recovery Educational Contentmentioning

confidence: 99%

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Recuperación inteligente de contenidos digitales educativos

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An empirical analysis of ontology-based query expansion for learning resource searches using MERLOT and the Gene ontology

Cited by 34 publications

References 39 publications

Smart System for the Retrieval of Digital Educational Content

Smart System for the Retrieval of Digital Educational Content

Curriculum and instruction

Recuperación inteligente de contenidos digitales educativos

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