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Category Archives: Projects
AKSW Colloquium, 15.02.2016, Mandolin + X-Feasible
On the 15th of February at 3 PM, Tommaso Soru will present his ongoing research titled “Mandolin: Markov Logic Networks for Discovering Links”. Later on, at 3:30 PM, our guest Adnan Akhter from HFT Stuttgart will present “X-FEASIBLE: Extended Feature-Based … Continue reading
Posted in Colloquium, invited talk, LinkingLOD, paper presentation
Tagged benchmarks, entity linking, Link Discovery, linked data, Papers, statistical relational learning
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DL-Learner 1.2 (Supervised Structured Machine Learning Framework) Released
Dear all, we are happy to announce DL-Learner 1.2. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can … Continue reading
Posted in DL-Learner, Uncategorized
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AKSW Colloquium, 01.02.2016, Co-evolution of RDF Datasets
At the todays colloquium, Natanael Arndt will discuss the the paper “Co-evolution of RDF Dataset” by Sidra Faisal, Kemele M. Endris, Saeedeh Shekarpour and Sören Auer (2016, available on arXiv) Link: http://arxiv.org/abs/1601.05270v1 Abstract: For many use cases it is not … Continue reading
Posted in Colloquium, LEDS, paper presentation
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HOBBIT project kick-off
HOBBIT, a new InfAI project within the EU’s “Horizon 2020” framework program kicked-off in Luxembourg on 18 and 19 january in 2016. The main goal of the HOBBIT project (@hobbit_project on Twitter) is to benchmark linked and big data systems and assess … Continue reading
Posted in Announcements, HOBBIT, project kick-off
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LinkedGeoData: New RDF versions of OpenStreetMap datasets available
The AKSW research group is happy to announce that a new LinkedGeoData maintenance release with more than 1.2 billion triples based on the OpenStreetMap planet file from 2015-11-02 is now online. Enjoy! Quick Links Project Website Downloads SPARQL Endpoint Virtual … Continue reading
Posted in Announcements, Linked Geo Data, Software Releases
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AKSW Colloquium, 09-11-2015, Versioning of Arbitrary RDF Data (PhD progress report) and GraphLab Platform
GraphLab Platform – Overview and History by Simon Bin GraphLab is a graph-based distributed computation framework. It was developed from 2009 at Carnegie Mellon University. At that time it was competing with Hadoop on Graph processing. The typical example algorithm demonstrated … Continue reading
Posted in Colloquium, LEDS, LUCID, PHD progress report
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AKSW Colloquium, 14 September, 3pm, Learning Metrics for Link Discovery
In this Colloquium, Tommaso Soru will present the progress of his PhD titled “Learning Metrics for Link Discovery”. The discovery of new links is essential for the construction of the Linked Data cloud. The use of links to other URIs … Continue reading
Posted in Colloquium, LIMES, PHD progress report, PhD topic
Tagged key discovery, Link Discovery, Machine Learning
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DL-Learner 1.1 (Supervised Structured Machine Learning Framework) Released
Dear all, we are happy to announce DL-Learner 1.1. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can … Continue reading
Posted in Announcements, DL-Learner, Software Releases
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AKSW Colloquium, 20-07-2015, Enterprise Linked Data Networks
Enterprise Linked Data Networks (PhD progress report) by Marvin Frommhold The topic of the thesis is the scientific utilization of the LUCID research project, in particular the LUCID Endpoint Prototype. In LUCID we research and develop on Linked Data technologies in order to … Continue reading
Posted in Colloquium, LUCID, PHD progress report
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AKSW Colloquium, 15-06-2015, Caching for Link Discovery
Using Caching for Local Link Discovery on Large Data Sets [PDF] by Mofeed Hassan Engineering the Data Web in the Big Data era demands the development of time- and space-efficient solutions for covering the lifecycle of Linked Data. As shown … Continue reading
Posted in Colloquium, LIMES, Papers
Tagged caching, large datasets, LIMES, Link Discovery
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