A Quantitative Analysis of the Use of Microdata for Semantic Annotations on Educational Resources

A current trend in the semantic web is the use of embedded markup formats aimed to semantically enrich web content by making it more understandable to search engines and other applications. The deployment of Microdata as a markup format has increased thanks to the widespread of a controlled vocabulary provided by Schema.org. Recently, a set of properties from the Learning Resource Metadata Initiative (LRMI) specification, which describes educational resources, was adopted by Schema.org. These properties, in addition to those related to accessibility and the license of resources included in Schema.org, would enable search engines to provide more relevant results in searching for educational resources for all users, including users with disabilities. In order to obtain a reliable evaluation of the use of Microdata properties related to the LRMI specification, accessibility, and the license of resources, this research conducted a quantitative analysis of the deployment of these properties in large-scale web corpora covering two consecutive years. The corpora contain hundreds of millions of web pages. The results further our understanding of this deployment in addition to highlighting the pending issues and challenges concerning the use of such properties

Retrieval, crawling and fusion of entity-centric data on the web

While the Web of (entity-centric) data has seen tremendous growth over the past years, take-up and re-use is still limited. Data vary heavily with respect to their scale, quality, coverage or dynamics, what poses challenges for tasks such as entity retrieval or search. This chapter provides an overview of approaches to deal with the increasing heterogeneity of Web data. On the one hand, recommendation, linking, profiling and retrieval can provide efficient means to enable discovery and search of entity-centric data, specifically when dealing with traditional knowledge graphs and linked data. On the other hand, embedded markup such as Microdata and RDFa has emerged a novel, Web-scale source of entitycentric knowledge. While markup has seen increasing adoption over the last few years, driven by initiatives such as schema.org, it constitutes an increasingly important source of entity-centric data on the Web, being in the same order of magnitude as the Web itself with regards to dynamics and scale. To this end, markup data lends itself as a data source for aiding tasks such as knowledge base augmentation, where data fusion techniques are required to address the inherent characteristics of markup data, such as its redundancy, heterogeneity and lack of links. Future directions are concerned with the exploitation of the complementary nature of markup data and traditional knowledge graphs. The final publication is available at Springer via http://dx.doi.org/ 10.1007/978-3-319-53640-8_1

The use of accessibility metadata in e‑learning environments: a systematic literature review

E-learning environments constitute an essential element in education, as they help students to ensure their pass their courses and graduate on time. Although guidelines, techniques, and methods have been presented in some literature in recent years to contribute to the development of accessible e-learning environments that promote digital inclusion, their implementation is challenging. In this context, the use of accessibility metadata not only provides a way to enhance the description of adapted educational resources but also facilitates their search according to the needs and preferences of students, in particular those with disabilities. In this paper, a systematic review was conducted in order to provide the state of the art regarding the use of accessibility metadata in e-learning environments. A total of 746 documents were found during the period from 2012 to 2019, of which 31 were selected according to the inclusion and exclusion criteria relevant to our review. The findings revealed an intensive use of models and standards of accessibility in e-learning environments, however, using accessibility metadata remains underused. In fact, the evaluation of accessibility and adaptability through the use of its metadata was not found. The fndings obtained also helped to establish challenges and opportunities in this research feld as well as to provide an overview that could support those who generate educational resources to keep their interest in making them accessible.publishedVersio

A Semantics-based User Interface Model for Content Annotation, Authoring and Exploration

The Semantic Web and Linked Data movements with the aim of creating, publishing and interconnecting machine readable information have gained traction in the last years. However, the majority of information still is contained in and exchanged using unstructured documents, such as Web pages, text documents, images and videos. This can also not be expected to change, since text, images and videos are the natural way in which humans interact with information. Semantic structuring of content on the other hand provides a wide range of advantages compared to unstructured information. Semantically-enriched documents facilitate information search and retrieval, presentation, integration, reusability, interoperability and personalization. Looking at the life-cycle of semantic content on the Web of Data, we see quite some progress on the backend side in storing structured content or for linking data and schemata. Nevertheless, the currently least developed aspect of the semantic content life-cycle is from our point of view the user-friendly manual and semi-automatic creation of rich semantic content. In this thesis, we propose a semantics-based user interface model, which aims to reduce the complexity of underlying technologies for semantic enrichment of content by Web users. By surveying existing tools and approaches for semantic content authoring, we extracted a set of guidelines for designing efficient and effective semantic authoring user interfaces. We applied these guidelines to devise a semantics-based user interface model called WYSIWYM (What You See Is What You Mean) which enables integrated authoring, visualization and exploration of unstructured and (semi-)structured content. To assess the applicability of our proposed WYSIWYM model, we incorporated the model into four real-world use cases comprising two general and two domain-specific applications. These use cases address four aspects of the WYSIWYM implementation: 1) Its integration into existing user interfaces, 2) Utilizing it for lightweight text analytics to incentivize users, 3) Dealing with crowdsourcing of semi-structured e-learning content, 4) Incorporating it for authoring of semantic medical prescriptions

Enhancing Humanities Research Productivity in a Collaborative Data Sharing Environment

This project represents a multi-organizational, interdisciplinary effort to enhance collaborative research in cultural heritage fields by exploring user experience with Web-based technologies. The objective of this project is to document user needs around online systems for sharing primary data and documentation of cultural heritage collections. To this end, we will draw upon the experience and insights of representatives from different stakeholder groups in three broad arenas: academic researchers, heritage managers, and specialist communities. Investigations undertaken in this study will result in best-practice guidelines to guide humanities computing efforts on how to best meet the diverse user needs in future online data sharing systems. Using an iterative cycle of development, deployment, and evaluation, this project will enhance Open Context, a collaborative, open-access data sharing system already in use for archaeology and related disciplines