HOBBIT: Holistic Benchmarking of Big Linked Data

HOBBIT is a European project that develops a holistic open-source platform and industry-grade benchmarks for benchmarking big linked data.




Big Data is one of the key assets of the future. However, the cost and efforts required for introducing Big Data technology in a value chain is significant. Mastering the creation of value from Big Data will enhance European competitiveness will result in economic growth and jobs and will deliver societal benefit. To facilitate the use of Big Linked Data, the European Union funds a research and innovation project called "HOBBIT". A European consortium, led by the Institute for Applied Informatics (InfAI) e.V., aims to develop a holistic benchmarking platform for big linked data and corresponding industry-grade benchmarks.


A key step towards abolishing the barriers to the adoption and deployment of Big Data is to provide European companies with open benchmarking reports that allow them to assess the fitness of existing solutions for their purposes. Achieving this goal demands: The deployment of benchmarks on data that reflects reality within realistic settings. The provision of corresponding industry-relevant key performance indicators. The computation of comparable results on standardized hardware.


HOBBIT aims to address these tasks by means of a strong team composed of leading research institutes, large industry customers and innovative small and medium-sized enterprises. In particular, the consortium will aim to achieve the following goals: Define benchmarks for domains of industrial relevance in Europe that make use of Big Linked Data. Determine the key performance indicators for processing Big Linked Data by collaborating with stakeholders from industry and research. Create an open benchmarking platform to evaluate the performance of state-of-the-art systems on standardized hardware. Organize yearly evaluation campaigns, using the platform and the industry-defined KPIs.


HOBBIT is a project within the EU’s "Horizon 2020" framework program and started on December 1st, 2015. The consortium consists of InfAI (coordinator, Germany), Fraunhofer IAIS (Germany), FORTH (Greece), NCSR "Demokritos" (Greece), iMinds (Belgium), USU Software AG (Germany), Ontos AG (Switzerland), OpenLink Software (UK), AGT Group R&D GmbH (Germany) and TomTom (Poland).

  • Duration: 12/2015–11/2018
  • Funding Programme: EU H2020 Research and Innovation Program


by (Editors: ) [BibTex of ]


SANSA 0.2 (Semantic Analytics Stack) Released ( 2017-06-13T18:18:28+02:00 by Prof. Dr. Jens Lehmann)

2017-06-13T18:18:28+02:00 by Prof. Dr. Jens Lehmann

The AKSW and Smart Data Analytics groups are happy to announce SANSA 0.2 – the second release of the Scalable Semantic Analytics Stack. Read more about "SANSA 0.2 (Semantic Analytics Stack) Released"

AKSW at ESWC 2017 ( 2017-06-12T10:53:35+02:00 Christopher Schulz)

2017-06-12T10:53:35+02:00 Christopher Schulz

Hello Community! The ESWC 2017 just ended and we give a short report of the course at the conference, especially regarding the AKSW-Group. Our members Dr. Muhammad Saleem, Dr. Mohamed Ahmed Sherif, Claus Stadler, Michael Röder, Prof. Dr. Read more about "AKSW at ESWC 2017"

Four papers accepted at WI 2017 ( 2017-06-10T15:01:31+02:00 Christopher Schulz)

2017-06-10T15:01:31+02:00 Christopher Schulz

Hello Community! We proudly announce that The International Conference on Web Intelligence (WI) accepted four papers by our group. The WI takes place in Leipzig between the 23th – 26th of August. Read more about "Four papers accepted at WI 2017"

AKSW Colloquium, 29.05.2017, Addressing open Machine Translation problems with Linked Data. ( 2017-05-26T13:51:11+02:00 by Diego Moussallem)

2017-05-26T13:51:11+02:00 by Diego Moussallem

At the AKSW Colloquium, on Monday 29th of May 2017, 3 PM, Diego Moussallem will present two papers related to his topic. First paper titled “Using BabelNet to Improve OOV Coverage in SMT” of Du et al. Read more about "AKSW Colloquium, 29.05.2017, Addressing open Machine Translation problems with Linked Data."

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"