It is widely accepted that proper data publishing is difficult. The majority of Linked Open Data (LOD) does not meet even a core set of data publishing guidelines. Moreover, datasets that are clean at creation, can get stains over time. As a result, the LOD cloud now contains a high level of dirty data that is difficult for humans to clean and for machines… (More)
Contemporary Semantic Web research is in the business of optimizing algorithms for only a handful of datasets such as DBpedia, BSBM, DBLP and only a few more. This means that current practice does not generally take the true variety of Linked Data into account. With hundreds of thousands of datasets out in the world today the results of Semantic Web… (More)
Ad-hoc querying is crucial to access information from Linked Data, yet publishing queryable RDF datasets on the Web is not a trivial exercise. The most compelling argument to support this claim is that the Web contains hundreds of thousands of data documents, while only 260 queryable SPARQL end-points are provided. Even worse, the SPARQL endpoints we do… (More)
General human intelligence is needed in order to process Linked Open Data (LOD). On the Semantic Web (SW), content is intended to be machine-processable as well. But the extent to which a machine is able to navigate, access, and process the SW has not been extensively researched. We present LOD Observer, a web observatory that studies the Web from a machine… (More)
The DynaLearn interactive learning environment allows learners to construct their conceptual ideas and investigate the logical consequences of those ideas. By building and simulating causal models, students develop an understanding of how systems work. The DynaLearn interactive learning environment introduces six modes of interaction, called learning… (More)
The Semantic Web (SW) was originally positioned as a combination of Knowledge Representation (KR) and the Web. However, most applications that use SW data today lean more towards the Information Retrieval spectrum. The reason for this is that traditional KR systems are designed to work with datasets that are small, curated, homogeneous, and… (More)
DynaLearn is an Interactive Learning Environment that facilitates a constructive approach to developing a conceptual understanding of how systems work. The software can be put in different interactive modes facilitating alternative learning experiences, and as such provides a toolkit for educational research.