Papers by Laurens De Vocht
Lecture Notes in Computer Science, 2014
As the Web of Data is growing at an ever increasing speed, the lack of reliable query solutions f... more As the Web of Data is growing at an ever increasing speed, the lack of reliable query solutions for live public data becomes apparent. sparql implementations have matured and deliver impressive performance for public sparql endpoints, yet poor availability-especially under high loads-prevents their use in real-world applications. We propose to tackle this availability problem by defining triple pattern fragments, a specific kind of Linked Data Fragments that enable low-cost publication of queryable data by moving intelligence from the server to the client. This paper formalizes the Linked Data Fragments concept, introduces a client-side sparql query processing algorithm that uses a dynamic iterator pipeline, and verifies servers' availability under load.
Nowadays Learning Management Systems are an integrated part of educational institutions. Teachers... more Nowadays Learning Management Systems are an integrated part of educational institutions. Teachers as well as learners profit from the so-called Web 2.0 applications in their daily learning process. Communication and collaboration between students have been enhanced using mashups of Web 2.0 technologies. Smart mobile phones and the increased availability of free wireless network access points make the integration of all these tools in our personal daily life and personal learning process much easier than before. This publication focuses on the Personal Learning Environment (PLE) that was launched at Graz University of Technology (TU Graz) in 2010. It illustrates how the PLE at TU Graz has been extended to move towards mobile PLE. Furthermore the learning activities of about more than 4000 learners in the last two years are revealed based on the tracked user behavior. The activities and usage traces are modeled using domain specific semantic ontologies. The models are used as the input for our Analytics Dashboard to visualize statistics and get a quick overview of learning habits and overall reflection usages and activity dynamics in the PLE.
Lecture Notes in Computer Science, 2013
We report on the reflection of learning activities and revealing hidden information based on trac... more We report on the reflection of learning activities and revealing hidden information based on tracked user behaviour in our widget based PLE (Personal Learning Environment) at Graz University of Technology. Our reference data set includes information of more then 4000 active learners for a period of around two years. We have modelled activity and usage traces using domain specific ontologies like Activity Ontology and Learning Context Ontology from the IntelLEO 1 EU project. Generally we distinguish three different metrics: user centric, learning object (widget) centric and activity centric. We have used Semantic Web query languages like SPARQL and representation formats like RDF to implement a human and machine readable web service along with a learning analytics dashboard for metrics visualization. The results offer a quick overview of learning habits, preferred set-ups of learning objects (widgets) and overall reflection of usages and activity dynamics in the PLE platform over time. The architecture delivers insights for intervening and recommending as closure of a learning analytics cycle[1] to optimize confidence in the PLE.
We report on the reflection of learning activities and revealing hidden information based on trac... more We report on the reflection of learning activities and revealing hidden information based on tracked user behaviour in our widget based PLE (Personal Learning Environment) at Graz University of Technology. Our reference data set includes information of more then 4000 active learners for a period of around two years. We have modelled activity and usage traces using domain specific ontologies like Activity Ontology and Learning Context Ontology from the IntelLEO 1 EU project. Generally we distinguish three different metrics: user centric, learning object (widget) centric and activity centric. We have used Semantic Web query languages like SPARQL and representation formats like RDF to implement a human and machine readable web service along with a learning analytics dashboard for metrics visualization. The results offer a quick overview of learning habits, preferred set-ups of learning objects (widgets) and overall reflection of usages and activity dynamics in the PLE platform over time. The architecture delivers insights for intervening and recommending as closure of a learning analytics cycle[1] to optimize confidence in the PLE.
Open Data which concisely and unambiguously describes a knowledge domain. However, the uptake of ... more Open Data which concisely and unambiguously describes a knowledge domain. However, the uptake of the Linked Data depends on its usefulness to non-Semantic Web experts. Failing to support data consumers to understand the added-value of Linked Data and possible exploitation opportunities could inhibit its diffusion. In this paper, we propose an interactive visual workflow for discovering and exploring Linked Open Data. We implemented the workflow considering academic library metadata and carried out a qualitative evaluation. We assessed the workflow's potential impact on data consumers which bridges the offer: published Linked Open Data; and the demand as requests for: (i) higher quality data; and (ii) more applications that re-use data. More than 70% of the 34 test users agreed that the workflow fulfills its goal: it facilitates non-Semantic Web experts to understand the potential of Linked Open Data.
Computer, 2014
Open Governments use the Web as a global dataspace for datasets. It is in the interest of these g... more Open Governments use the Web as a global dataspace for datasets. It is in the interest of these governments to be interoperable with other governments worldwide, yet there is currently no way to identify relevant datasets to be interoperable with and there is no way to measure the interoperability itself. In this article we discuss the possibility of comparing identifiers used within various datasets as a way to measure semantic interoperability. We introduce three metrics to express the interoperability between two datasets: the identifier interoperability, the relevance and the number of conflicts. The metrics are calculated from a list of statements which indicate for each pair of identifiers in the system whether they identify the same concept or not. While a lot of effort is needed to collect these statements, the return is high: not only relevant datasets are identified, also machine-readable feedback is provided to the data maintainer.
ABSTRACT As the Web evolves in an integrated and interlinked knowledge space thanks to the growin... more ABSTRACT As the Web evolves in an integrated and interlinked knowledge space thanks to the growing amount of published Linked Open Data, the need to find solutions that enable the scholars to discover, explore and analyse the underlying research data emerges. Scholars, typically non-expert technology users, lack of in-depth understanding of the underlying semantic technology which limits their ability to interpret and query the data. We present a visual workflow to connect scholars and scientific resources on the Web of Data. We allow scholars to move from exploratory analysis in academic social networks to exposing relations between these resources. We allow them to reveal experts in a particular field and discover relations in and beyond their research communities. This paper aims to evaluate the potential of such a visual workflow to be used by non-expert users to interact with the semantically enriched data and familiarize with the underlying dataset.
Communications in Computer and Information Science, 2015
Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies - i-KNOW '11, 2011
We propose a framework to address an important issue in the context of the ongoing adoption of th... more We propose a framework to address an important issue in the context of the ongoing adoption of the "Web 2.0" in science and research, often referred to as "Science 2.0" or "Research 2.0". A growing number of people are linked via acquaintances and online social networks such as Twitter 1 allows indirect access to a huge amount of ideas. These ideas are contained in a massive human information flow . That users of these networks produce relevant data is being shown in many studies [1][2][28] . The problem however lies in discovering and verifying such a stream of unstructured data items. Another related problem is locating an expert that could provide an answer to a very specific research question. We are using semantic technologies (RDF 2 ,SPARQL 3 ) , common vocabularies(SIOC 4 , FOAF 5 ,SWRC 6 ) and Linked Data (DBpedia 7 , GeoNames 8 , CoLinDa 9 ) [3] [5] to extract and mine the data about scientific events out of context of microblogs. Hereby we are identifying persons and organization related to them based on entities of time, place and topic. The framework provides an API that allows quick access to the information that is analyzed by our system. As a proof-of-concept we explain, implement and evaluate such a researcher profiling use case. It involves the development of a framework that focuses on the
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Papers by Laurens De Vocht