Smart Data Web: Creation of an industry knowledge base for the German industry.

The Smart Data Web project has to goal to create an industry knowledge base for the German industry.


Smart Data Web is a BMWi funded project. The central goal of the Smart Data Web project is to leverage state-of-the-art data extraction and enrichment technologies as well as Linked Data to create value-added systems for the German industry. Knowledge which is relevant to decision-making processes will be extracted from government and industry data, official web pages and social media. Then the data will be analyzed using natural language processing frameworks and then it will be integrated into knowledge graphs. These knowledge graphs will be accessible via dashboards and APIs, as well as via Linked Data. Special concern will be given to legal questions, such as data licensing as well as data security and privacy.

AKSW, which is representing the University of Leipzig in this project, will develop the Knowledge Graph, which is the central aggregation and integration interface of Smart Data Web. Unlike most current Linked Data knowledge bases, the German Knowledge Graph will focus on industry-relevant data. The graph will be developed in an iterative extraction, integration and interlinking process, building on proven technologies of the Linked Data Stack. Data quality and persistence are a special priority of the German Knowledge Graph since consistency has to be guaranteed at all times. RDFUnit is our tool of choice to accomplish this task.

Smart Data Web will contribute significantly to overcome the barriers that hinder the integration of Semantic Web technologies, Web 2.0 data and data analysis for commercial applications.


SML-Bench 0.2 Released ( 2017-05-11T13:01:45+02:00 by Patrick Westphal)

2017-05-11T13:01:45+02:00 by Patrick Westphal

Dear all, we are happy to announce the 0.2 release of SML-Bench, our Structured Machine Learning benchmark framework. SML-Bench provides full benchmarking scenarios for inductive supervised machine learning covering different knowledge representation languages like OWL and Prolog. Read more about "SML-Bench 0.2 Released"

AKSW Colloquium, 08.05.2017, Scalable RDF Graph Pattern Matching ( 2017-05-08T09:42:49+02:00 by Lorenz Bühmann)

2017-05-08T09:42:49+02:00 by Lorenz Bühmann

At the AKSW Colloquium, on Monday 8th of May 2017, 3 PM, Lorenz Bühmann will discuss a paper titled “Type-based Semantic Optimization for Scalable RDF Graph Pattern Matching” of Kim et al. Read more about "AKSW Colloquium, 08.05.2017, Scalable RDF Graph Pattern Matching"

ESWC 2017 accepted two Demo Papers by AKSW members ( 2017-04-19T10:19:43+02:00 Christopher Schulz)

2017-04-19T10:19:43+02:00 Christopher Schulz

Hello Community! The 14th ESWC, which takes place from May 28th to June 1st 2017 in Portoroz, Slovenia, accepted two demos to be presented at the conference. Read more about them in the following:                                                                         1. Read more about "ESWC 2017 accepted two Demo Papers by AKSW members"

AKSW Colloquium, 10.04.2017, GeoSPARQL on geospatial databases ( 2017-04-07T10:43:55+02:00 by Dr. Matthias Wauer)

2017-04-07T10:43:55+02:00 by Dr. Matthias Wauer

At the AKSW Colloquium, on Monday 10th of April 2017, 3 PM, Matthias Wauer will discuss a paper titled “Ontop of Geospatial Databases“. Read more about "AKSW Colloquium, 10.04.2017, GeoSPARQL on geospatial databases"

AKSW Colloquium, 03.04.2017, RDF Rule Mining ( 2017-03-31T13:39:28+02:00 TommasoSoru)

2017-03-31T13:39:28+02:00 TommasoSoru

At the AKSW Colloquium, on Monday 3rd of April 2017, 3 PM, Tommaso Soru will present the state of his ongoing research titled “Efficient Rule Mining on RDF Data”, where he will introduce Horn Concerto, a novel scalable SPARQL-based approach … Continue reading → Read more about "AKSW Colloquium, 03.04.2017, RDF Rule Mining"