Groups

Data Integration

The Linked Data paradigm builds upon the backbone of distributed knowledge bases connected by typed links. The mere volume of current knowledge bases as well as their sheer number pose two major challenges when aiming to support the integration of data across and within them. The first is that tools for data integration have to be time-efficient specially when to deal with big datasets. Secondly, these tools have to carry out the data integration tasks of high quality to serve the applications built upon Linked Data well. Our solutions to the second problem build upon efficient computational approaches developed to solve the first and combine these with dedicated machine learning techniques. All our frameworks for data integration such as LIMES and DEER are open-source and available under a GNU license at https: //github.com/dice-group together with user/developer manuals.

Data Storage and Querying

The constant growth of Linked Data on the Web gives rise to new challenges for querying and integrating massive amounts of data. Such datasets are available through various interfaces, such as data dumps, Linked Data documents and webpages, SPARQL endpoints, Triple Pattern Fragments, or the Linked Data Platform. In addition, various sources produce streaming data. Efficiently querying these sources is of central importance for the scalability of Linked Data and Semantic Web technologies. To exploit the massive amount of data to its full potential, users should be able to store, query, and combine this data easily and effectively. The DSQ group develops scalable and high performance RDF sytems for storing and querying Big RDF data. In addition, we are also working on knowledge extraction from the Web and their RDF graph modelling. Finally, we are keen to design most representative benchmarsk pertaining to the storing, querying, and extracting RDF data.

Interpretable Data Processing

The Interpretable Data Processing group works on two main fields.

Firstly, we are gathering, preparing and analysing Linked Data. The first part of this pipeline is done by using our open-source crawler Squirrel. This crawler has been used in several projects, including the two research projects OPAL and LIMBO. After data has been gathered, we provide several Fact Checking services including COPAAL, FactCheck, HybridFC, TemporalFC, and FAVEL, which can be used to ensure the veracity of the data with respect to a reference knowledge base or a reference corpus. We also apply these tools in our research project NEBULA.

The second main field this group is working on is benchmarking. We are maintaining several benchmarking platforms and tools:

HOBBIT is a holistic benchmarking platform for Big Linked Data solutions.
GERBIL is a light-weight platform for benchmarking web services. Currently we are supporting the benchmarking within three areas:
IGUANA is a benchmarking platform for evaluating the performance of triple stores.
Palmetto is an evaluation tool for topic models.

We also cover additional research areas that are connected to benchmarking like

The explainability of benchmarking results,
The generation of synthetic benchmarking data mimiking real-world data, and
Evaluating the robustness of Linked-Data-based approaches.

Lead

Michael Röder

Members

Denis Kuchelev Farshad Afshari Umair Qudus Neha Pokharel Hardik Shetty Rishikesh Yadav

Projects

ENEXA GERBIL Iguana NEBULA ORCA SAIL Squirrel

Alumni Projects

FROCKG HOBBIT KnowGraphs LIMBO OPAL

Demos

GERBIL KE GERBIL QA GERBIL KBC HOBBIT Platform Palmetto Demo

Machine Learning

The machine learning group focuses on machine learning technologies for knowledge graphs. The research goal is to develop novel, scalable machine learning algorithms, e.g., for entity embeddings, clustering of high-dimensional data, and explainable machine learning. Research activities of the ML group are in the scope of structured machine learning, e.g., learning concepts in description logics, and entity embeddings, e.g., based on physical models and convolutional complex models. The group provides open-source tools for machine learning on knowledge graphs.

Lead

Stefan Heindorf

Members

Caglar Demir Hamada Zahera N'Dah Jean Kouagou

Alumni

Adrian Wilke Geraldo de Souza Leonie N. Sieger Lukas Blübaum

Projects

COLIDE KIAM TRR 318

Alumni Projects

EML4U DAIKIRI RAKI

NLP and Data Access

Our group works on the intersection of Data Science and Natural Language Processing areas. We focus on creating algorithms that allow computers to extract automatically large-scale knowledge from unstructured data and process them while preserving their semantic key information. We aim to make the acquired knowledge accessible and understandable for both humans and computers. Our team has started addressing two of the most important tasks in NLP by relying on Knowledge Graphs, Named Entity Recognition, and Entity Linking. Our research resulted in two state-of-the-art frameworks in respect of multilingualism and knowledge-graph-based algorithms. Recently, we expanded our focus on different NLP tasks ranging from basic research in computational linguistics to Question Answering, Machine Translation, Natural Language Generation, and Understanding.

Our group includes members of both Paderborn and Leipzig universities.

Lead

Diego Moussallem

Members

Ria Hari Gusmita Asep Fajar Firmansyah Daniel Vollmers René Speck Nikit Srivastava

Alumni

Paramjot Kaur Sourabh Poddar Nandeesh Patel

Projects

SPEAKER PORQUE

Alumni Projects

LIMBO SOLIDE QAMEL DIESEL RAKI DAIKIRI KnowGraphs

Demos

FOX AGDISTIS TeBaQA IDA