Local Type Checking for Linked Data Consumers

The Web of Linked Data is the cumulation of over a decade of work by the Web standards community in their effort to make data more Web-like. We provide an introduction to the Web of Linked Data from the perspective of a Web developer that would like to build an application using Linked Data. We identify a weakness in the development stack as being a lack of domain specific scripting languages for designing background processes that consume Linked Data. To address this weakness, we design a scripting language with a simple but appropriate type system. In our proposed architecture some data is consumed from sources outside of the control of the system and some data is held locally. Stronger type assumptions can be made about the local data than external data, hence our type system mixes static and dynamic typing. Throughout, we relate our work to the W3C recommendations that drive Linked Data, so our syntax is accessible to Web developers.Comment: In Proceedings WWV 2013, arXiv:1308.026

Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners

In this paper, we suggest a novel method to aid lifelong learners to access relevant OER based learning content to master skills demanded on the labour market. Our software prototype 1) applies Text Classification and Text Mining methods on vacancy announcements to decompose jobs into meaningful skills components, which lifelong learners should target; and 2) creates a hybrid OER Recommender System to suggest personalized learning content for learners to progress towards their skill targets. For the first evaluation of this prototype we focused on two job areas: Data Scientist, and Mechanical Engineer. We applied our skill extractor approach and provided OER recommendations for learners targeting these jobs. We conducted in-depth, semi-structured interviews with 12 subject matter experts to learn how our prototype performs in terms of its objectives, logic, and contribution to learning. More than 150 recommendations were generated, and 76.9% of these recommendations were treated as useful by the interviewees. Interviews revealed that a personalized OER recommender system, based on skills demanded by labour market, has the potential to improve the learning experience of lifelong learners.Comment: This paper has been accepted to be published in the proceedings of CSEDU 2020 by SciTePres

A BASILar Approach for Building Web APIs on top of SPARQL Endpoints

The heterogeneity of methods and technologies to publish open data is still an issue to develop distributed systems on the Web. On the one hand, Web APIs, the most popular approach to offer data services, implement REST principles, which focus on addressing loose coupling and interoperability issues. On the other hand, Linked Data, available through SPARQL endpoints, focus on data integration between distributed data sources. The paper proposes BASIL, an approach to build Web APIs on top of SPARQL endpoints, in order to benefit of the advantages from both Web APIs and Linked Data approaches. Compared to similar solution, BASIL aims on minimising the learning curve for users to promote its adoption. The main feature of BASIL is a simple API that does not introduce new specifications, formalisms and technologies for users that belong to both Web APIs and Linked Data communities

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Detailed empirical studies of student information storing in the context of distributed design team-based project work

This paper presents the findings of six empirical case studies investigating the information stored by engineering design students in distributed team-based Global Design Projects. The aim is to understand better how students store distributed design information in order to prepare them for work in today‟s international and global context. This paper outlines the descriptive element of the work, the qualitative and quantitative research methods used and the results. It discusses the issues around the emergent themes of information storing; information storing systems; information storing patterns; and information strategy, making recommendations; establishing that there is a need for more prescriptive measures to supporting distributed design information management. This work will be of great value to industry also

An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations

Workflow formalisations are often focused on the representation of a process with the primary objective to support execution. However, there are scenarios where what needs to be represented is the effect of the process on the data artefacts involved, for example when reasoning over the corresponding data policies. This can be achieved by annotating the workflow with the semantic relations that occur between these data artefacts. However, manually producing such annotations is difficult and time consuming. In this paper we introduce a method based on recommendations to support users in this task. Our approach is centred on an incremental rule association mining technique that allows to compensate the cold start problem due to the lack of a training set of annotated workflows. We discuss the implementation of a tool relying on this approach and how its application on an existing repository of workflows effectively enable the generation of such annotations