A user profile definition in context of recommendation of open educational resources. An approach based on linked open vocabularies

Open Educational Resources include a diverse range of materials making it the most representative icon arisen within the Open Content movement. Users who access and use OERs could be classified into one of these three groups: instructor, student and self-learner. To provide personalized lists of OERs according to the user profile and personal preferences, the user should be characterized by an open and scalable model. In this paper, an open linked vocabulary is proposed to describe user profiles of the open educational resources, which take into account the challenges and opportunities that an open and extensible platform as the Web can provide to learn about the OER users, and from this knowledge, offer the most appropriate resource

Towards personalization in digital libraries through ontologies

In this paper we describe a browsing and searching personalization system for digital libraries based on the use of ontologies for describing the relationships between all the elements which take part in a digital library scenario of use. The main goal of this project is to help the users of a digital library to improve their experience of use by means of two complementary strategies: first, by maintaining a complete history record of his or her browsing and searching activities, which is part of a navigational user profile which includes preferences and all the aspects related to community involvement; and second, by reusing all the knowledge which has been extracted from previous usage from other users with similar profiles. This can be accomplished in terms of narrowing and focusing the search results and browsing options through the use of a recommendation system which organizes such results in the most appropriate manner, using ontologies and concepts drawn from the semantic web field. The complete integration of the experience of use of a digital library in the learning process is also pursued. Both the usage and information organization can be also exploited to extract useful knowledge from the way users interact with a digital library, knowledge that can be used to improve several design aspects of the library, ranging from internal organization aspects to human factors and user interfaces. Although this project is still on an early development stage, it is possible to identify all the desired functionalities and requirements that are necessary to fully integrate the use of a digital library in an e-learning environment

Semantic web approach for italian graduates' surveys: the AlmaLaurea ontology proposal

Il crescente sviluppo e la promozione della trasparenza dei dati nell’ambito della pubblica amministrazione copre molteplici aspetti, fra cui l’educazione universitaria. Attualmente sono difatti numerosi i dataset rilasciati in formato Linked Open Data disponibili a livello nazionale ed internazionale. Fra le informazioni pubblicamente disponibili spiccano concetti riguardo l’occupazione e la numerosità dei laureati. Nonostante il progresso riscontrato, la mancanza di una metodologia standard per la descrizione di informazioni statistiche sui laureati rende difficoltoso un confronto di determinati fatti a partire da differenti sorgenti di dati. Sul piano nazionale, le indagini AlmaLaurea colmano il gap informativo dell’eterogeneità delle fonti proponendo statistiche centralizzate su profilo dei laureati e relativa condizione occupazionale, aggiornate annualmente. Scopo del progetto di tesi è la realizzazione di un’ontologia di dominio che descriva diverse peculiarità dei laureati, promuovendo allo stesso tempo la definizione strutturata dei dati AlmaLaurea e la successiva pubblicazione nel contesto Linked Open Data. Il progetto, realizzato con l’ausilio delle tecnologie del Web Semantico, propone infine la creazione di un endpoint SPARQL e di una interfaccia web per l'interrogazione e la visualizzazione dei dati strutturati

DRIVER Technology Watch Report

This report is part of the Discovery Workpackage (WP4) and is the third report out of four deliverables. The objective of this report is to give an overview of the latest technical developments in the world of digital repositories, digital libraries and beyond, in order to serve as theoretical and practical input for the technical DRIVER developments, especially those focused on enhanced publications. This report consists of two main parts, one part focuses on interoperability standards for enhanced publications, the other part consists of three subchapters, which give a landscape picture of current and surfacing technologies and communities crucial to DRIVER. These three subchapters contain the GRID, CRIS and LTP communities and technologies. Every chapter contains a theoretical explanation, followed by case studies and the outcomes and opportunities for DRIVER in this field

A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems

Personalisation, adaptation and recommendation are central features of TEL environments. In this context, information retrieval techniques are applied as part of TEL recommender systems to filter and recommend learning resources or peer learners according to user preferences and requirements. However, the suitability and scope of possible recommendations is fundamentally dependent on the quality and quantity of available data, for instance, metadata about TEL resources as well as users. On the other hand, throughout the last years, the Linked Data (LD) movement has succeeded to provide a vast body of well-interlinked and publicly accessible Web data. This in particular includes Linked Data of explicit or implicit educational nature. The potential of LD to facilitate TEL recommender systems research and practice is discussed in this paper. In particular, an overview of most relevant LD sources and techniques is provided, together with a discussion of their potential for the TEL domain in general and TEL recommender systems in particular. Results from highly related European projects are presented and discussed together with an analysis of prevailing challenges and preliminary solutions.LinkedU

Content Recommendation Through Linked Data

Nowadays, people can easily obtain a huge amount of information from the Web, but often they have no criteria to discern it. This issue is known as information overload. Recommender systems are software tools to suggest interesting items to users and can help them to deal with a vast amount of information. Linked Data is a set of best practices to publish data on the Web, and it is the basis of the Web of Data, an interconnected global dataspace. This thesis discusses how to discover information useful for the user from the vast amount of structured data, and notably Linked Data available on the Web. The work addresses this issue by considering three research questions: how to exploit existing relationships between resources published on the Web to provide recommendations to users; how to represent the user and his context to generate better recommendations for the current situation; and how to effectively visualize the recommended resources and their relationships. To address the first question, the thesis proposes a new algorithm based on Linked Data which exploits existing relationships between resources to recommend related resources. The algorithm was integrated into a framework to deploy and evaluate Linked Data based recommendation algorithms. In fact, a related problem is how to compare them and how to evaluate their performance when applied to a given dataset. The user evaluation showed that our algorithm improves the rate of new recommendations, while maintaining a satisfying prediction accuracy. To represent the user and their context, this thesis presents the Recommender System Context ontology, which is exploited in a new context-aware approach that can be used with existing recommendation algorithms. The evaluation showed that this method can significantly improve the prediction accuracy. As regards the problem of effectively visualizing the recommended resources and their relationships, this thesis proposes a visualization framework for DBpedia (the Linked Data version of Wikipedia) and mobile devices, which is designed to be extended to other datasets. In summary, this thesis shows how it is possible to exploit structured data available on the Web to recommend useful resources to users. Linked Data were successfully exploited in recommender systems. Various proposed approaches were implemented and applied to use cases of Telecom Italia

EduCOR: An Educational and Career-Oriented Recommendation Ontology

With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations. Resource: https://tibonto.github.io/educor/

Content Enrichment of Digital Libraries: Methods, Technologies and Implementations

Parallel to the establishment of the concept of a "digital library", there have been rapid developments in the fields of semantic technologies, information retrieval and artificial intelligence. The idea is to use make use of these three fields to crosslink bibliographic data, i.e., library content, and to enrich it "intelligently" with additional, especially non-library, information. By linking the contents of a library, it is possible to offer users access to semantically similar contents of different digital libraries. For instance, a list of semantically similar publications from completely different subject areas and from different digital libraries can be made accessible. In addition, the user is able to see a wider profile about authors, enriched with information such as biographical details, name alternatives, images, job titles, institute affiliations, etc. This information comes from a wide variety of sources, most of which are not library sources. In order to make such scenarios a reality, this dissertation follows two approaches. The first approach is about crosslinking digital library content in order to offer semantically similar publications based on additional information for a publication. Hence, this approach uses publication-related metadata as a basis. The aligned terms between linked open data repositories/thesauri are considered as an important starting point by considering narrower, broader and related concepts through semantic data models such as SKOS. Information retrieval methods are applied to identify publications with high semantic similarity. For this purpose, approaches of vector space models and "word embedding" are applied and analyzed comparatively. The analyses are performed in digital libraries with different thematic focuses (e.g. economy and agriculture). Using machine learning techniques, metadata is enriched, e.g. with synonyms for content keywords, in order to further improve similarity calculations. To ensure quality, the proposed approaches will be analyzed comparatively with different metadata sets, which will be assessed by experts. Through the combination of different information retrieval methods, the quality of the results can be further improved. This is especially true when user interactions offer possibilities for adjusting the search properties. In the second approach, which this dissertation pursues, author-related data are harvested in order to generate a comprehensive author profile for a digital library. For this purpose, non-library sources, such as linked data repositories (e.g. WIKIDATA) and library sources, such as authority data, are used. If such different sources are used, the disambiguation of author names via the use of already existing persistent identifiers becomes necessary. To this end, we offer an algorithmic approach to disambiguate authors, which makes use of authority data such as the Virtual International Authority File (VIAF). Referring to computer sciences, the methodological value of this dissertation lies in the combination of semantic technologies with methods of information retrieval and artificial intelligence to increase the interoperability between digital libraries and between libraries with non-library sources. By positioning this dissertation as an application-oriented contribution to improve the interoperability, two major contributions are made in the context of digital libraries: (1) The retrieval of information from different Digital Libraries can be made possible via a single access. (2) Existing information about authors is collected from different sources and aggregated into one author profile.Parallel zur Etablierung des Konzepts einer „Digitalen Bibliothek“ gab es rasante Weiterentwicklungen in den Bereichen semantischer Technologien, Information Retrieval und künstliche Intelligenz. Die Idee ist es, mit ihrer Hilfe bibliographische Daten, also Inhalte von Bibliotheken, miteinander zu vernetzen und „intelligent“ mit zusätzlichen, insbesondere nicht-bibliothekarischen Informationen anzureichern. Durch die Verknüpfung von Inhalten einer Bibliothek wird es möglich, einen Zugang für Benutzer*innen anzubieten, über den semantisch ähnliche Inhalte unterschiedlicher Digitaler Bibliotheken zugänglich werden. Beispielsweise können hierüber ausgehend von einer bestimmten Publikation eine Liste semantisch ähnlicher Publikationen ggf. aus völlig unterschiedlichen Themenfeldern und aus verschiedenen digitalen Bibliotheken zugänglich gemacht werden. Darüber hinaus können sich Nutzer*innen ein breiteres Autoren-Profil anzeigen lassen, das mit Informationen wie biographischen Angaben, Namensalternativen, Bildern, Berufsbezeichnung, Instituts-Zugehörigkeiten usw. angereichert ist. Diese Informationen kommen aus unterschiedlichsten und in der Regel nicht-bibliothekarischen Quellen. Um derartige Szenarien Realität werden zu lassen, verfolgt diese Dissertation zwei Ansätze. Der erste Ansatz befasst sich mit der Vernetzung von Inhalten Digitaler Bibliotheken, um auf Basis zusätzlicher Informationen für eine Publikation semantisch ähnliche Publikationen anzubieten. Dieser Ansatz verwendet publikationsbezogene Metadaten als Grundlage. Die verknüpften Begriffe zwischen verlinkten offenen Datenrepositorien/Thesauri werden als wichtiger Angelpunkt betrachtet, indem Unterbegriffe, Oberbegriffe und verwandten Konzepte über semantische Datenmodelle, wie SKOS, berücksichtigt werden. Methoden des Information Retrieval werden angewandt, um v.a. Publikationen mit hoher semantischer Verwandtschaft zu identifizieren. Zu diesem Zweck werden Ansätze des Vektorraummodells und des „Word Embedding“ eingesetzt und vergleichend analysiert. Die Analysen werden in Digitalen Bibliotheken mit unterschiedlichen thematischen Schwerpunkten (z.B. Wirtschaft und Landwirtschaft) durchgeführt. Durch Techniken des maschinellen Lernens werden hierfür Metadaten angereichert, z.B. mit Synonymen für inhaltliche Schlagwörter, um so Ähnlichkeitsberechnungen weiter zu verbessern. Zur Sicherstellung der Qualität werden die beiden Ansätze mit verschiedenen Metadatensätzen vergleichend analysiert wobei die Beurteilung durch Expert*innen erfolgt. Durch die Verknüpfung verschiedener Methoden des Information Retrieval kann die Qualität der Ergebnisse weiter verbessert werden. Dies trifft insbesondere auch dann zu wenn Benutzerinteraktion Möglichkeiten zur Anpassung der Sucheigenschaften bieten. Im zweiten Ansatz, den diese Dissertation verfolgt, werden autorenbezogene Daten gesammelt, verbunden mit dem Ziel, ein umfassendes Autorenprofil für eine Digitale Bibliothek zu generieren. Für diesen Zweck kommen sowohl nicht-bibliothekarische Quellen, wie Linked Data-Repositorien (z.B. WIKIDATA) und als auch bibliothekarische Quellen, wie Normdatensysteme, zum Einsatz. Wenn solch unterschiedliche Quellen genutzt werden, wird die Disambiguierung von Autorennamen über die Nutzung bereits vorhandener persistenter Identifikatoren erforderlich. Hierfür bietet sich ein algorithmischer Ansatz für die Disambiguierung von Autoren an, der Normdaten, wie die des Virtual International Authority File (VIAF) nachnutzt. Mit Bezug zur Informatik liegt der methodische Wert dieser Dissertation in der Kombination von semantischen Technologien mit Verfahren des Information Retrievals und der künstlichen Intelligenz zur Erhöhung von Interoperabilität zwischen Digitalen Bibliotheken und zwischen Bibliotheken und nicht-bibliothekarischen Quellen. Mit der Positionierung dieser Dissertation als anwendungsorientierter Beitrag zur Verbesserung von Interoperabilität werden zwei wesentliche Beiträge im Kontext Digitaler Bibliotheken geleistet: (1) Die Recherche nach Informationen aus unterschiedlichen Digitalen Bibliotheken kann über einen Zugang ermöglicht werden. (2) Vorhandene Informationen über Autor*innen werden aus unterschiedlichsten Quellen eingesammelt und zu einem Autorenprofil aggregiert

A case study on TUdatalib

Semantic Web and Linked Data technologies might solve issues originating from research data being published by independent providers. For maximum benefit from these technologies, metadata should be provided as standardized as possible. The Data Catalog Vocabulary (DCAT) is a W3C recommendation of potential value for Linked Data exposure of research data metadata. The suitability of DCAT for institutional research data repositories was investigated using the TUdatalib repository as study case. A model for TUdatalib metadata was developed based on the analysis of selected resources and guided by a draft of DCAT 3. The model allowed for providing the essential information about the repository structure and contents indicating suitability of the vocabulary and, conceptually, should permit automated data conversion from the repository system to DCAT 3. A loss of expressiveness comes from the omission of dataset series. Conformance with DCAT 3 class definitions led to a highly complex model, thus creating challenges with actual technical realizations. A comparative study revealed simpler models to be used at two other repositories, but implementation of the TUdatalib or a similar model would have potential to improve alignment to DCAT specifications. DCAT 3 was observed to be a promising option for Linked Data exposure of institutional research data repository metadata and the TUdatalib model might serve towards developing a general DCAT 3 application profile for institutional and other research data repositories

a digital humanities platform to explore the Portuguese cultural heritage

LISBOA-01-0145-FEDER-022139The ROSSIO Infrastructure is developing a free and open-access platform for aggregating, organising, and connecting the digital resources in the Social Sciences, Arts and Humanities provided by Portuguese higher education and cultural institutions. This paper presents an overview of the ROSSIO Infrastructure, its main objectives, the institutions involved, and the services offered by the infrastructure’s aims through its platform—namely, a discovery portal, digital exhibitions, collections, and a virtual research environment. These services rely on a metadata-aggregation solution for bringing the digital objects’ metadata from the providing institutions into ROSSIO. The aggregated datasets are converted into linked data and undergo an enrichment process based on controlled vocabularies, which are developed and published by ROSSIO. The paper will describe this process, the applications involved, and how they interoperate. We will further reflect on how these services may enhance the dissemination of science, considering the FAIR principles.publishersversionpublishe