If you would like to contact the above team, please write an email to If you would like to contact all persons listed below, please write an email to For contacting individual persons, please visit their work pages below.

The following subgroups belong to AKSW

Adaptive Information and Knowledge Engineering

The AIKE group focuses on the application of Semantic Web technologies to support adaptive Information and Knowledge Engineering. The research goal is to use domain specific vocabularies for modelling of application software and compiling software components. To achieve results in this fields, research activities of the AIKE group are in the scope of agile and collaborative requirements engineering, knowledge extraction from existing databases, knowledge engineering, and knowledge alignment to software component interfaces. The group also provides established open source tools and use cases in the field of digital humanities. Read more about Adaptive Information and Knowledge Engineering

Emergent Semantics

Research in Emergent Semantics revolves around supporting semantic collaboration scenarios in an adaptive way. This work includes especially research on engineering and adoption of semantic collaboration software, semantic collaboration protocols as well as research on distributed / federated semantic social networks. To bootstrap semantic collaboration and the semantic web in general, the Emergent Semantics group also investigates basic semantic technologies and semantic web infrastructures. Read more about Emergent Semantics

Knowledge Integration and Linked Data Technologies

What kind of smart applications can we build, if we were able to integrate all available knowledge, data and language resources in a meaningful way? While Turing's imitation game is exciting, we are focussing on the actual knowledge engineering to build information machines that enable humans to perform more efficiently in their tasks. To achieve this goal, we believe the following two prerequisites must be met:

  1. Knowledge and Data must be rendered discoverable and then transformed, linked, enriched and integrated homogeneously in a huge semantic knowledge graph (DBpedia)
  2. Language Technologies must be on the one hand leveraged to understand, categorize and structure available textual content in all its forms. On the other hand, language technology must assist in building adequate interfaces that allow humans to interact effectively with data and information via discovery, querying and reorganization.

Research in this group focusses on contextualising data and ontologies as well as capturing deep linguistic knowledge to improve machine understanding. Besides research, we are taking the extra effort to undertake transfer and innovation projects and support industrial applications such as in the JURION use case.

Main contact: Dr.-Ing. Sebastian Hellmann

Read more about Knowledge Integration and Linked Data Technologies

Machine Learning and Ontology Engineering

The MOLE group focuses on combining Semantic Web and supervised Machine Learning technologies. The goal is to improve both quality and quantity of available knowledge by extracting, analysing, enriching and linking existing data. To make obtained results readily available for use in other applications, the group also provides several established open source tools, frameworks and demonstrators. Read more about Machine Learning and Ontology Engineering

Semantic Abstraction

SIMBA's focus in supporting the transition of non-semantic applications to knowledge-driven applications. Hence we support all major steps from legacy data to rich semantic applications. This includes but is not limited to knowledge storage (triple stores, federated queries), knowledge extraction (RDF extraction from text, structured data, etc.), knowledge integration (link discovery, data fusion), knowledge access (keyword-based search, question answering and rich interfaces) and knowledge consumption within semantic applications . For this purpose, SIMBA develops novel and scalable approaches for data ranging from small to Big Data. In addition, SIMBA provides tools and frameworks that implement these approaches and allow for their swift integration into industry projects. Read more about Semantic Abstraction


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"