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Author Archives: Simon Bin
DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released
Dear all, The Smart Data Analytics group [1] and the E.T.-db-MOLE sub-group located at the InfAI Leipzig [2] is happy to announce DL-Learner 1.4. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can … Continue reading
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SANSA 0.3 (Semantic Analytics Stack) Released
Dear all, We are happy to announce SANSA 0.3 – the third release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing via Apache Spark and Flink in order to allow scalable machine learning, inference and querying capabilities for … Continue reading
AKSW Colloquium 30.Jan.2017
In the upcoming Colloquium, Simon Bin will discuss the paper “Towards Analytics Aware Ontology Based Access to Static and Streaming Data” by Evgeny Kharlamov et.al. that has been presented at ISWC2017. Abstract Real-time analytics that requires integration and aggregation … Continue reading
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AKSW Colloquium, 13.06.2016, SPARQL query processing with Apache Spark
In the upcoming Colloquium, Simon Bin will discuss the paper “SPARQL query processing with Apache Spark” by H. Naacke et.al. that has been submitted to ISWC2016. Abstract The number of linked data sources and the size of the linked open … Continue reading
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AKSW Colloquium, 01-06-2015, MEX – Publishing ML Experiment Results, Scaling DL-Learner – Status and Plans
MEX – Publishing ML Experiment Results by Diego Esteves Over the decades many machine learning experiments have been published, collaborating with the scientific community progress. One of the key-factors in order to compare machine learning experiment results to each other … Continue reading
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SAKE Projekt website goes live
Hi all! The project website for the BMWi funded Smart Data Web Project “SAKE” is now on-line at www.sake-projekt.de. It already mentions the first SAKE-related publication by Saleem@AKSW and introduces our partners as well as the industry use cases which … Continue reading
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