Linked TCGA: Linked TCGA

The Cancer Genome Atlas Database aims to characterize the changes that occur in genes due to cancer. Knowledge about such changes can be of central nature when aiming to predict the life expectancy as well as the medication or sequence of medication that should be administered to a patient to ensure his/her survival. So far, experts that needed this data had to wait in long data queues and write dedicated tools to analyze this data.

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The 20.4 billion triples in LinkedTCGA remedy these problems by being a five-star Linked Data representation of the data made publicly available by TCGA. Researchers now need solely a small set of SPARQL queries to gather data represented in a standard format (RDF). Linked TCGA publishes different types of data (clinical, exon, methylation, miRNA etc.). The data to each tumor is have distributed into 3 different SPARQL endpoints. The federated query engine is powered by TopFed, a dedicated engine for the TCGA data that was shown to outperform state-of-the-art approaches significantly (see publications). More information on the LinkedTCGA data can be found on the project website.

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News

SANSA 0.7.1 (Semantic Analytics Stack) Released ( 2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann)

2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann

We are happy to announce SANSA 0.7.1 – the seventh 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 large knowledge graphs. Read more about "SANSA 0.7.1 (Semantic Analytics Stack) Released"

More Complete Resultset Retrieval from Large Heterogeneous RDF Sources ( 2019-12-05T15:46:09+01:00 Andre Valdestilhas)

2019-12-05T15:46:09+01:00 Andre Valdestilhas

Over recent years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laundromat and SPARQL endpoints provide access to hundreds of thousands of RDF datasets, representing billions of facts. Read more about "More Complete Resultset Retrieval from Large Heterogeneous RDF Sources"

DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released ( 2019-09-24T22:41:46+02:00 by Simon Bin)

2019-09-24T22:41:46+02:00 by Simon Bin

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. Read more about "DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released"

DBpedia Day @ SEMANTiCS 2019 ( 2019-08-01T10:35:05+02:00 Sandra Bartsch)

2019-08-01T10:35:05+02:00 Sandra Bartsch

 We are happy to announce that SEMANTiCS 2019 will host the 14th DBpedia Community Meeting at the last day of the conference on September 12, 2019. Read more about "DBpedia Day @ SEMANTiCS 2019"

LDK conference @ University of Leipzig ( 2019-03-22T09:21:41+01:00 by Julia Holze)

2019-03-22T09:21:41+01:00 by Julia Holze

With the advent of digital technologies, an ever-increasing amount of language data is now available across various application areas and industry sectors, thus making language data more and more valuable. Read more about "LDK conference @ University of Leipzig"