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Prof. Dr. Han van der Aa
Prof. Dr. Christian Bizer
Prof. Dr. Rainer Gemulla
Prof. Dr. Goran Glavaš
Prof. Dr. Simone Paolo Ponzetto
Prof. Dr. Heiko Paulheim
Prof. Dr. Heiner Stuckenschmidt
Postdoctoral Research Fellows
Dr. Tobias Weller
Dr. Ines Rehbein
Dr. Ioana Hulpus
Dr. Melisachew Wudage Chekol
Dr. Christian Meilicke
Dr. Pedro Ortiz Suarez
Dr. Federico Nanni
Fabian David Schmidt
Affiliated PhD Students
Dr. Anna Primpeli
Dr. Anne Lauscher
Prof. Dr.-Ing. Margret Keuper
Dr. Taha Alhersh
Dr. Jakob Huber
Dr. Timo Sztyler
Dr. Dmitry Ustalov
Dr. Oliver Lehmberg
Dr. Yaser Oulabi
Dr. Kilian Theil
Web-based Systems (Prof. Bizer)
Data Analytics (Prof. Gemulla)
Web Data Mining (Prof. Paulheim)
Natural Language Processing and Information Retrieval (Prof. Ponzetto)
Artificial Intelligence (Prof. Stuckenschmidt)
Master Thesis Topics in Artificial Intelligence
Process Analytics (Prof. Van der Aa)
MMDS Industry Partner Network
Courses for Master Candidates
Data Analytics Team Project – Your Project, Your Team
CS 560 Large-Scale Data Management
CS 720 Seminar on Uncertainty Estimations
IE 500 Data Mining
IE 560 Decision Support
IE 650 Knowledge Graphs
IE 661 Text Analytics
IE 663 Information Retrieval and Web Search
IE 670 Web Data Integration
IE 671 Web Mining
IE 672 Data Mining 2
IE 675b Machine Learning
IE 678 Deep Learning
IE 689 Relational Learning
CS 460 Database Technology
CS 704 Artificial Intelligence Seminar
CS 704 Social Simulation Seminar
CS 709 Text Analytics Seminar
CS 710 Seminar on Explainable Artificial Intelligence
CS 707 Data and Web Science Seminar
CS 715: Large-Scale Data Integration Seminar
CS 718 AI and Data Science in Fiction and Society
CS 719 Process Analysis Seminar
IE 692 Advanced Process Mining
Courses for Bachelor Candidates
Praktische Informatik II
Einführung in Data Science
Wirtschaftsinformatik für WiPäds
Wirtschaftsinformatik für BaKuWis
Courses for PhD Candidates
Computational Text Analysis
Uni Mannheim Process Mining Meet-ups
University of Mannheim
Data and Web Science Group
DWS Area: Data Science
Paper accepted at AKBC
Our paper “Gollum: A Gold Standard for Large Scale Multi Source Knowledge Graph Matching” has been accepted at the 4th Conference on Automated Knowledge Graph Construction.
Best Paper Award at Semantics
Our paper “On a Generalized Framework for Time-Aware Knowledge Graphs”, co-authored by Franz Krause, Tobias Weller, and Heiko Paulheim, has been awarded the best paper award at this year's Semantics conference.
Jan Portisch has defended his PhD thesis
Jan Portisch has successfully defended his PhD thesis titled “Exploiting General-Purpose Background Knowledge for Automated Schema Matching” on August 25th.
Best Poster Award at ESWC 2022
The paper “Walk this Way! Entity Walks and Property Walks for RDF2vec” by Jan Portisch and Heiko Paulheim has won the best poster award at ESWC 2022.
Paper accepted at ISWC 2021
Our paper “Background Knowledge in Schema Matching: Strategy vs. Data” has been accepted at the 20th International Semantic Web Conference. The paper has been co-authored by Jan Portisch, Michael Hladik, and Heiko Paulheim. In the paper, we have conducted a number of controlled experiments with ...
Paper accepted at PAAMS
The paper “Winning at Any Cost – Infringing the Cartel Prohibition With Reinforcement Learning” was accepted at the 19th International Conference on Practical Applications of Agents and Multi-Agent Systems.
Paper accepted at JCDL
The paper “GraphConfRec: A Graph Neural Network-Based Conference Recommender System”, co-authored by Andreea Iana and Heiko Paulheim, has been accepted for publication at the ACM/
IEEE Joint Conference on Digital Libraries. In the paper, we explore the usage of modern graph neural network based ...
Best Demo Award at ESWC 2021
Earlier this year, we introduced kgextension, the Python extension for processing knowledge graphs. With this extension, data analysts can easily link their data at hand to both public and private knowledge graphs, and add the wealth of public knowledge graphs such as Wikidata to their data ...
Paper on Large-Scale Dataset Collection from Twitter accepted
Our paper “Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision” has been accepted for publication in Future Internet.
CaLiGraph version 2.0 released
CaLiGraph is an open knowledge graph extracted from categories, tables, and listings in Wikipedia.
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