Analyzing Cognitive Evolution using Linked Data: Towards Biomedical Data Integration for Analyzing the Evolution of Cognition

Cognition is determined by the function and interplay of several hundreds, if not thousands, of genes with a considerable overlap in the disease phenotypes they can cause if mutated. We argue that, in order to understand the biological basis of cognition, these genes have to be investigated together with their evolutionary history and the diseases they are implicated in. This requires the integration of data from different research disciplines. To allow researchers to answer complicated questions related to cognition, a task that is usually very time-consuming, we propose to use Linked Data publication. Such data integration and querying methods have already been successfully used in other life science domains. In our initial effort presented here, we converted and integrated 11 different datasets and provide a first demonstration of the added value of Linked Data by showing how a set of relevant queries over the integrated data can be answered.

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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"