Recommender systems (RS) are software tools that use analytic technologies to suggest different items of interest to an end user. Linked Data is a set of best practices for publishing and connecting structured data on the Web. This paper presents a systematic literature review to summarize the state of the art in RS that use structured data published as Linked Data for providing recommendations of items from diverse domains. It considers the most relevant research problems addressed and classifies RS according to how Linked Data have been used to provide recommendations. Furthermore, it analyzes contributions, limitations, application domains, evaluation techniques, and directions proposed for future research. We found that there are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications. The main ones are personalization of recommendations, use of more datasets considering the heterogeneity introduced, creation of new hybrid RS for adding information, definition of more advanced similarity measures that take into account the large amount of data in Linked Data datasets, and implementation of testbeds to study evaluation techniques and to assess the accuracy scalability and computational complexity of RS.Use URI (uniform resource identifiers) as names for things. Use HTTP (Hypertext Transfer Protocol) URIs, so that people can look up those names. Use of standard mechanisms to provide useful information when someone looks up a URI, for example, RDF (Resource Description Framework) to represent data as graphs and SPARQL (SPARQL Protocol and RDF Query Language) to query Linked Data. Include links to other URIs, so that they can discover more things.The main benefit of using Linked Data as a source for generating recommendations is the large amount of available concepts and the relationships between them that can be used to infer relations more effectively in comparison to derive the same kind of relationships from text [12]. As Linked Data information is machine-readable, it is possible to query datasets on a fine-grained level in order to collect information without having to take manual actions; therefore, information is explicitly represented, which allows for applying reasoning techniques when querying datasets and making implicit knowledge explicit. Recommender systemsRS are software tools and techniques that provide suggestions of items to a user. These items can belong to different categories or types, for example, songs, places, news, books, films, and events. ‡ 4661 According to Adomavicius and Tuzhilin [4], the roots of RS can be traced back to the works in cognitive science, approximation theory, information retrieval, forecasting theories, management science, and consumer choice modeling in marketing.Nowadays, RS are focused on the recommendation problem of guiding users in a personalized way to interesting items in a large space of possible options [10]. Typically, RS are classified as content based, collaborative filtering, knowledge based, and ...
The increase in the amount of structured data published on the Web using the principles of Linked Data means that now it is more likely to find resources on the Web of Data that represent real life concepts. Discovering and recommending resources on the Web of Data related to a given resource is still an open research area. This work presents a framework to deploy and execute Linked Data based recommendation algorithms to measure their accuracy and performance in different contexts. Moreover, application developers can use this framework as the main component for recommendation in various domains. Finally, this paper describes a new recommendation algorithm that adapts its behavior dynamically based on the features of the Linked Data dataset used. The results of a user study show that the algorithm proposed in this paper has better accuracy and novelty than other state-of-the-art algorithms for Linked Data.
The training curriculum for medical doctors requires the intensive and rapid assimilation of a lot of knowledge. To help medical students optimize their learning path, the SIDES 3.0 national French project aims to extend an existing platform with intelligent learning services. This platform contains a large number of annotated learning resources, from training and evaluation questions to students' learning traces, available as an RDF knowledge graph. In order for the platform to provide personalized learning services, the knowledge and skills progressively acquired by students on each subject should be taken into account when choosing the training and evaluation questions to be presented to them, in the form of customized quizzes. To achieve such recommendation, a first step lies in the ability to predict the outcome of students when answering questions (success or failure). With this objective in mind, in this paper we propose a model of the students' learning on the SIDES platform, able to make such predictions. The model extends a state-ofthe-art approach to fit the specificity of medical data, and to take into account additional knowledge extracted from the OntoSIDES knowledge graph in the form of graph embeddings. Through an evaluation based on learning traces for pediatrics and cardiovascular specialties, we show that considering the vector representations of answers, questions and students nodes substantially improves the prediction results compared to baseline models.
Educational quizzes are very valuable resources to test or evaluate the knowledge acquired by learners and to support lifelong learning on various topics or subjects, in an informal and entertaining way. The production of quizzes is a very time-consuming task and its automation is thus a real challenge in eEducation. In this paper, we address the research question of how to automate the generation of quizzes by taking advantage of existing knowledge sources available on the Web.We propose an approach that allows learners to take advantage of the knowledge captured in domain ontologies available on the Web and to discover or acquire a more in-depth knowledge of a specific domain by solving educational quizzes automatically generated from an ontology modelling the domain.The implementation and experimentation of our approach is presented through the use case of a worldfamous French game of manually generated multiple-choice questions.
Access control is a recognized open issue when interacting with RDF using HTTP methods. In literature, authentication and authorization mechanisms either introduce undesired complexity such as SPARQL and ad-hoc policy languages, or rely on basic access control lists, thus resulting in limited policy expressiveness. In this paper we show how the Shi3ld attribute-based authorization framework for SPARQL endpoints has been progressively converted to protect HTTP operations on RDF. We proceed by steps: we start by supporting the SPARQL 1.1 Graph Store Protocol, and we shift towards a SPARQL-less solution for the Linked Data Platform. We demonstrate that the resulting authorization framework provides the same functionalities of its SPARQL-based counterpart, including the adoption of Semantic Web languages only.
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