AKSW Blog

Crawling the Linked Open Data Cloud by Michael Röder

In the recent years the Linked Open Data Cloud matured to support various applications, e.g., question answering, knowledge inference or database enrichment. Such applications have to be supported by the recent data, which is impossible to retrieve without constant crawling, due to the dynamic nature of the World Wide Web and the cloud as a part of it. On this colloquium Michael is going to talk about the state of the art for Linked Data Crawling as well as the future work that will be done by Ivan Ermilov, Axel-Cyrille Ngonga Ngomo and himself.

Hybrid Question Answering at QALD 5 challenge by Ricardo Usbeck

The plethora of datasets on the web, both structured and unstructured, enables answering complex questions such as “Which anti-apartheid activist was born in Mvezo?” Some of those hybrid (source) question answering system have been benchmarked at the QALD 5 challenge at CLEF conference. Ricardo is going to present some of the results and give future research directions.

Slides: https://docs.google.com/presentation/d/1dccMwbPMIeOpzvV1PNCKKxg96xZBSdK2Gav9JynAJjo/edit?usp=sharing

BDE, Hadoop MapR and HDFS by Hajira Jabeen

Hajira will present brief introduction to BigData Europe project (BDE). Followed by , Hadoop HDFS and map reduce for distributed processing of large data sets on compute clusters of commodity hardware. Hadoop is one of the many Big Data components being used in the BDE project.

About the AKSW Colloquium

This event is part of a series of events about Semantic Web technology. Please see http://wiki.aksw.org/Colloquium for further information about previous and future events. As always, Bachelor and Master students are able to get points for attendance and there is complimentary coffee and cake after the session.

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 and collaborate positively is to thoroughly perform them on the same computing environment using the same sample data sets and algorithms configurations. Besides, practical experience shows that scientists and engineers tend to have too many output data for their sets of experiments, which, in the end, is either difficult to be analyzed without a provenance metadata as well as archive properly. Despite the efforts for publishing and managing these variables accordingly, we still have a knowledge gap, which is explicated by a missing public ontology for machine learning experiments in order to achieve the interoperability for published results. In order to minimize this gap, we introduce the novel MEX Ontology, built up based on the W3C PROV Ontology (PROV-O) and following the nanopublication principles.

Scaling DL-Learner – Status and Plans by Simon Bin

With the event of Big Data and Large Scale Data Processing, new challenges also are approaching the DL-Learner. The framework for supervised Machine Learning on OWL and RDF may benefit from various approaches to make it work better with huge data. In this talk, first experimental results of using SPARQL instead of traditional OWL-Reasoner approaches will be shown and possible future directions for scaling DL-Learner will be sketched.

About the AKSW Colloquium

This event is part of a series of events about Semantic Web technology. Please see http://wiki.aksw.org/Colloquium for further information about previous and future events. As always, Bachelor and Master students are able to get points for attendance and there is complimentary coffee and cake after the session.