Professor Christian Bizer is a leading figure in web-based systems and data integration at the University of Mannheim , where he chairs Information Systems V: Web-based Systems . His research focuses on integrating data from multiple sources using large language models and LLM-based agents, with applications in product data extraction and DBpedia knowledge graph construction. He co-founded the DBpedia project and initiated the WebDataCommons initiative. Current research areas: Entity matching, schema matching, table annotation, information extraction, data discovery Key projects: WebMall benchmark, WInte.r integration framework, Schema.org analysis His work applies to e-commerce data integration and knowledge graph construction, with empirical studies on schema.org adoption. He supervises PhD students including Alexander Brinkmann and Ralph Peeters. Scientific Awards: Best Paper at iiWAS 2024 SWSA Ten-Year Award at ISWC 2019 Yahoo FREP Award 2015 Semantic Web Challenge winners Teaching includes courses on web data integration, web mining, large language models, and data mining for master's programs. He leads the DWS PhD colloquium and team projects on LLM agents for data integration.
Max Planck Institute of Colloids and InterfacesGermany
Dr. Jay Pujara is a Research Associate Professor of Computer Science at the University of Southern California (USC) and Director of the Center on Knowledge Graphs. He is also a Principal Scientist at the Information Sciences Institute (ISI) and leads research teams in data science and AI. Ph.D., University of Maryland, College Park (2016) M.S. and B.S. in Computer Science, Carnegie Mellon University Research Interests include artificial intelligence, probabilistic models, knowledge graph construction, statistical relational learning, NLP, and streaming inference. His work focuses on scalable algorithms for big data and uncertainty modeling in dynamic environments. Recent Publications highlight advancements in knowledge graphs, LLM reasoning, and table understanding. Notable topics include non-verbal abstract reasoning , faithful conversational datasets , and KGQA re-ranking . Scientific Awards : SWSA Ten-Year Award (2023), Outstanding Paper (IUI 2019), Top Reviewer (NeurIPS 2018), Best Paper (SRL Workshop 2016) Advising & Grants : Mentored 12+ graduate students, including Ph.D. advisees on topics like causal modeling and neuro-symbolic tasks. Secured NSF funding for table understanding in paleoclimate studies.
Simon Razniewski is a Professor of Knowledge-based Artificial Intelligence at TU Dresden and ScaDS.AI, focusing on integrating language models and knowledge bases. He previously held roles at Bosch Center for AI (2023–2024), Max Planck Institute for Informatics (2017–2021), and Free University of Bozen-Bolzano (2014–2017). His research spans knowledge extraction, computational logic, and data science applications. He holds a PhD (2014) and Diplom (MSc, 2010) from Free University of Bozen-Bolzano and TU Dresden, respectively. Research interests include large language models (LLMs), knowledge graph construction, and uncertainty quantification in natural language processing. His work bridges AI theory and practice, with publications at top venues like ACL and EMNLP. He teaches courses on LLMs and knowledge-aware AI, and has advised numerous collaborative projects across academia and industry. Prominent contributions include frameworks like GPTKB for LLM knowledge materialization and QUITE for Bayesian reasoning in NLP. His prior roles at Siemens IT and Globalfoundries inform his applied research focus. The International Center for Computational Logic (ICCL) at TU Dresden is his primary research hub, emphasizing interdisciplinary computational logic and AI advancements.
Professor Alsayed Algergawy is the substitute for the Chair of Data and Knowledge Engineering at the University of Passau since April 2023. His work bridges semantic web technologies and machine learning to enable heterogeneous data integration across domains. Current focus areas: schema/ontology alignment, entity resolution, knowledge graph construction Active in DFG Collaborative Research Center AquaDiva (data lifecycle management) Domain applications: biodiversity, agriculture, energy His team develops hybrid strategies combining rule-based systems with ML techniques to extract value from both structured and unstructured data sources.
Prof. Dr. Wolfgang Nejdl is a Professor at the Institute for Data Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He serves as Executive Director of the L3S Research Centre and Leibniz Forschungszentrum Inclusive Citizenship. Web Science Information Retrieval Artificial Intelligence Deep Learning His recent research focuses on AI applications in medicine , multimodal data fusion , and ethical AI systems . Projects include CAIMed (AI in Causal Medicine) and DAISEC (AI & Cybersecurity). His publications span conferences like AAMAS, WWW, and SIGIR. Notable awards include membership in the National Academy of Science and Engineering (acatech) . Former students hold positions at institutions like Stanford, TU Dresden, and ETH Zürich. Current projects involve climate resilience AI , federated learning for healthcare , and quantum-inspired data science .
Birgitta König-Ries is a Professor at the Department of Computer Science, University of Jena, Germany. She is a leading researcher in semantic technologies, ontology engineering, and knowledge graph management for biodiversity and life sciences. Her work focuses on reproducibility, provenance tracking, and data integration using semantic approaches. Research Interests: Semantic Web, Ontology Engineering, Knowledge Graphs, Biodiversity Informatics, Reproducibility of Scientific Experiments Key Collaborations: Sheeba Samuel, Nora Abdelmageed, Samira Babalou, Alsayed Algergawy, Felicitas Löffler, Vamsi Krishna Kommineni Her recent publications emphasize automated knowledge graph construction, domain-specific language models (e.g., BiodivBERT), benchmarking semantic table interpretation (KG2Tables, BiodivTab), and tools for provenance management (MLProvLab, MLProvCodeGen). She contributes to FAIR data principles and interdisciplinary research, particularly in biodiversity and public administration transparency. Her work bridges theoretical advances with practical implementations, including open-access benchmarks (tFood, tBiodiv, tBiomed) and collaborative platforms like BiodivPortal and fusion-jena. Notable Tools & Benchmarks: BiodivBERT, KG2Tables, BiodivTab, MLProvLab, tBiodiv, tBiomed
Schloss Dagstuhl - Leibniz Center for InformaticsGermany
Jiaoyan Chen is a Senior Lecturer (Associate Professor) in the Department of Computer Science at The University of Manchester. She previously held roles as a Lecturer at Manchester, a Senior Researcher at the University of Oxford, and a Postdoctoral Fellow at Heidelberg University. Education: PhD and Bachelor's in Computer Science and Technology from Zhejiang University (2016 and 2011), with a visiting PhD stint at Zurich University's Department of Informatics. Research Interests: Integrating knowledge graphs and ontologies with machine learning and large language models (LLMs), focusing on semantic embeddings, knowledge curation, and explainable AI systems. Publication Trends show emphasis on ontology embeddings (e.g., OWL2Vec*), LLM evaluation with knowledge graphs, and hybrid neural-symbolic reasoning. Her work bridges structured knowledge and modern AI through projects like OntoEm and ConCur . Current Research Team includes postdoctoral researchers, PhD students, and externally co-supervised associates. She actively recruits PhD candidates in areas like Retrieval-Augmented Generation and LLM Explainability , with projects funded by EPSRC and international consortia. Grants & Leadership: EPSRC New Investigator Award (2023-2026) Manchester-Melbourne-Toronto Research Fund (2024-2026) EPSRC ConCur Project (2021-2025) Professional Service: Associate Editor, Transactions on Graph Data and Knowledge EPSRC Peer Review College member OAEI Track Co-organizer at ISWC
Prof. Dr. Heiko Paulheim is a Professor of Data Science and currently serves as University Vice President at the University of Mannheim. He leads the Data and Web Science Group (DWS), which focuses on Web Data Mining, Knowledge Graphs, and Semantic Web technologies. His research group contributes to open source knowledge graphs like DBpedia and develops new knowledge graphs such as WebIsALOD and DBkWik. As of October 1, 2024, he has limited teaching capacity due to his vice presidential duties. Prof. Paulheim's research interests span Knowledge Graphs, Semantic Web, Web Data Mining, Machine Learning, and Natural Language Processing. His work particularly focuses on knowledge graph refinement, embedding techniques (notably RDF2vec), and applications in various domains including news recommendation, biomedical informatics, and environmental monitoring. His group develops practical tools like the RapidMiner Linked Open Data Extension and RDF2vec for knowledge graph applications. His recent publications demonstrate a strong focus on knowledge graph embeddings, with particular attention to RDF2vec variants, applications in news recommendation systems, biomedical data integration, and spatio-temporal knowledge graphs for environmental monitoring. His work bridges theoretical advances in knowledge representation with practical applications across multiple domains. Among his notable achievements are a nomination for the Best Paper Award at CAiSE 2025 and securing an Open Science Grant for the SpatialBenchRAG project. His research has significant impact in both academic and industrial contexts, with multiple papers accepted at top conferences like ISWC and ESWC. Prof. Paulheim has supervised numerous PhD students including Alexander Brinkmann and Michael Schlechtinger, and has led several research projects including the DFG Project Mine@LOD, State of BW Project SyKoW², and BMBF Project DS4DM. His group maintains strong industry connections with partners like SAP AG, Daimler AG, and IDS.
Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols
Prof. Dr. Axel-Cyrille Ngonga Ngomo is a Professor at the University of Paderborn , affiliated with the Faculty of Electrical Engineering, Computer Science and Mathematics and the Institute of Computer Science . He leads the Data Science group at the Heinz Nixdorf Institute and is a member of the Sonderforschungsbereich Transregio 318 (Constructing Explainability). His roles include heading the Informatik Rechnerbetrieb (IRB) team. Research Focus : Knowledge graphs, semantic web technologies, explainable AI, and distributed systems. Selected Projects : SAIL (Sustainable Life Cycle of Intelligent Sociotechnical Systems), TRR 318 (Constructing Explainability), Colide (Co-training for Industrial Data), 3DFed (Dynamic Data Distribution), and SFB 901 (On-The-Fly Computing). Contact : Email axel.ngonga@uni-paderborn.de , Office F1.225 (Fürstenallee 11) and TP6.3.106 (Technologiepark 6), Paderborn. Teaching : Courses include Seminar on Recent Advances in Knowledge Graphs, Project Groups on SPARQL Query Processing, Large Language Model Training, Retrieval Augmented Generation, and Foundations of Knowledge Graphs.
Prof. Dr. Jakob Beetz serves as a University Professor at RWTH Aachen University's Faculty of Architecture, leading the Design Computation (DC) research group. His work addresses critical challenges in sustainable built environments through digital innovation, focusing on integrating knowledge, information, and data across disciplines to reduce the sector's energy and material consumption—which accounts for over one-third of global totals—while advancing climate goals under the European Green Deal. His research spans Building Information Modeling (BIM), digital twins, and artificial intelligence, with emphasis on graph-based data federation, semantic web technologies, and large language models in construction. Key interests include evidence-based planning, parametric design optimization, building physics simulation, and networked knowledge modeling. Recent projects explore federated digital twin ecosystems for infrastructure management, intelligent damage assessment systems, and AI-driven solutions for wood structure preservation, directly contributing to sustainable development targets. Analysis of his 2024-2025 publications reveals a cohesive trajectory toward decentralized data environments and AI integration in Architecture, Engineering, and Construction (AEC). His work bridges theoretical foundations in knowledge representation with practical applications in bridge maintenance, road infrastructure, and timber construction, demonstrating consistent innovation in spatial data querying, federated issue management, and ontology-based process modeling. Prof. Beetz actively supervises PhD candidates, as evidenced by DC.Promotions 2024, and drives international collaboration through events like the Forum Construction Informatics 2025 and CIB W78 conferences. His research group engages with industry standards including Industry Foundation Classes (IFC) and Common Data Environments (CDEs), emphasizing open data principles and interoperability to transform construction workflows.
Max Planck Institute of Colloids and InterfacesGermany
Craig Knoblock serves as Keston Executive Director of the Information Sciences Institute (ISI) at the University of Southern California (USC), Vice Dean of the USC Viterbi School of Engineering, and Research Professor of Computer Science and Spatial Sciences. He also directs the Data Science Program and the Center on Knowledge Graphs at USC. His educational background includes a Ph.D. and M.S. in Computer Science from Carnegie Mellon University (1991, 1988) and a B.S. with honors in Computer Science from Syracuse University (1984). Knoblock's research focuses on data semantics , specializing in source modeling, schema and ontology alignment, entity and record linkage, data cleaning, Web data extraction, and knowledge graph construction. His work bridges computer science, geospatial analysis, and artificial intelligence to solve complex data integration challenges. Recent projects emphasize historical map digitization, geospatial knowledge graphs, and smart city applications. His 300+ publications demonstrate consistent contributions to knowledge graphs and geospatial data integration, with a growing emphasis on historical map analysis and urban applications. The research trajectory shows increasing interdisciplinary collaboration across computer vision, geoinformatics, and domain-specific applications. IEEE Fellow (2020) ACM Fellow (2017) AAAI Fellow (2004) Robert S. Engelmore Memorial Lecture Award (2014) Donald E. Walker Distinguished Service Award (IJCAI, 2018) Use-Inspired Research Award (USC Viterbi, 2018) As Executive Director of ISI, Knoblock oversees one of USC's premier research centers with significant federal funding. His leadership extends to directing the Center on Knowledge Graphs and the Data Science Program. While specific grant details aren't provided, his extensive publication record and leadership roles indicate substantial research funding across data integration, knowledge representation, and geospatial applications. His work bridges theoretical computer science with practical applications in historical preservation, urban planning, and resource management through collaborative projects with government agencies and industry partners. Knoblock leads the Center on Knowledge Graphs at USC, focusing on developing techniques for building and utilizing knowledge graphs across diverse domains. His team combines expertise in artificial intelligence, geospatial analysis, and data integration to tackle challenges in historical map digitization, urban applications, and resource discovery. The research group maintains strong connections with both academic and government partners through the Information Sciences Institute's extensive network.
Schloss Dagstuhl - Leibniz Center for InformaticsGermany
Pierre Monnin is a Junior Fellow in AI at Université Côte d'Azur , conducting research within the Wimmics team at the I3S Laboratory . He also teaches within the EFELIA Côte d'Azur program. His work spans multiple institutions through funded projects like SHACKLE (EU Horizon), ECLADATTA and AT2TA (ANR), with collaborations at Télécom Paris , Università di Bari , and INESC-ID in Lisbon. Previous roles include temporary lecturer at TELECOM Nancy (2023-2024) and researcher at Orange (2020-2023). Research Interests focus on the knowledge graph lifecycle (construction, matching, refinement, mining, discovery) from neurosymbolic AI and analogical reasoning perspectives. He explores Domain knowledge injection into ML models Symbolic-semantics for graph embeddings Zero-shot bootstrapping techniques Context-aware semantic annotation Link prediction with constraint enrichment Life sciences applications Recent scientific awards include: Best Paper Award at ESWC 2024 (Student & Resource Papers) Best Thesis Award from French Association EGC (2022) 1st Prize (Accuracy Track) at Semantic Web Challenge (2021) His teaching portfolio covers AI fundamentals for foreign languages, marketing, and adult education programs, with specialized courses in Semantic Web technologies NoSQL databases XML tools Compiler implementation He supervises multiple PhD students and interns on topics involving neurosymbolic refinement , knowledge reconciliation , and analogical reasoning . Key software contributions include: KGPrune - Web application for thematic Wikidata subgraph extraction PyGraft - Synthetic knowledge graph generation tool DAGOBAH UI - Semantic table interpretation interface He also maintains datasets like PGxLOD and YAGO4-LP for pharmacogenomics and link prediction.
Stefan Decker is a full University Professor (Universitätsprofessor) at RWTH Aachen University, Germany, where he heads the Chair of Information Systems and Databases (Informatik 5) within the Faculty of Mathematics, Computer Science and Natural Sciences. He is actively involved in teaching, research, and the supervision of numerous ongoing and completed doctoral, master’s, and bachelor theses. Education & Academic Background Doctorate (Dr. rer. pol.) – field of Information Systems or related (exact institution/year not stated in text). Appointed as University Professor and Chair of Information Systems and Databases at RWTH Aachen University. Research Interests Prof. Decker’s work lies at the intersection of databases, knowledge graphs, semantic web technologies, data science, and cybersecurity . He investigates architectures and algorithms for large-scale, privacy-preserving, decentralized data analytics , develops ontology-driven information systems , and explores the use of large language models (LLMs) for educational technology, anomaly detection, and incident-response playbooks. Additional focal areas include smart energy systems, mixed-reality learning environments, FAIR data principles, and federated machine learning . Scientific Contributions & Trends His recent publications (2022-2025) demonstrate a clear trend toward explainable AI, LLM-enhanced systems, secure data spaces, and semantic interoperability . Key contributions include novel anomaly-detection frameworks for encrypted power-grid communications, knowledge-graph-driven chatbots for higher-education support, and methodological advances in decentralized analytics and FAIR data sharing. These works are disseminated in top-tier venues such as AAAI, IEEE ISGT Europe, ESWC, IDEAL, and various Springer LNCS and IEEE Transactions. Supervision & Grants Doctoral Theses Advised: A. T. Neumann – “Chatbots as professional companions in large-scale community information systems” (2024) S. M. Welten – “Methods for practical data sharing and decentralized analytics” (2025) Master’s Theses Co-Advised: A. R. Küsters – “Object-centric process constraints using variable bindings” (2025) Additionally supervising more than 30 ongoing bachelor, master, and doctoral projects covering topics such as LLM-driven cybersecurity playbooks, knowledge-graph construction for German law, privacy-preserving analytics in smart grids, and mixed-reality learning agents. Principal investigator or senior researcher in large collaborative projects including NFDI4DS, WestAI, champI4.0ns and several EU/national initiatives on sovereign data spaces and AI services. Labs & Teams Prof. Decker leads the Information Systems & Databases (DBIS) Research Group . The group operates well-equipped laboratories for knowledge-graph engineering, mixed-reality applications, privacy-enhancing technologies, and secure distributed analytics . Current team size exceeds 30 researchers including PhD candidates, postdocs, and scientific programmers, supported by national and EU funding streams.
Schloss Dagstuhl - Leibniz Center for InformaticsGermany
Catia Pesquita is an Associate Professor in Computer Science at the Faculty of Sciences of the University of Lisbon , where she is also a Senior Researcher at LASIGE and leads the Health and Biomedical Informatics Research Line . With a multidisciplinary background in Biology and Computer Science, she focuses on Artificial Intelligence and Data Science applications in life and health sciences . Her research spans Semantic Web , Biomedical Ontologies , Knowledge Graphs , and Explainable AI , with significant contributions to ontology matching and semantic similarity . Education: PhD in Computer Science - Bioinformatics (2012) MSc in Bioinformatics (2008) Degree in Cell Biology and Biotechnology (2005) Current Projects: KATY (2021-2024): AI-Empowered Personalized Medicine for cancer treatments. BRAINTEASER (2021-2024): AI for ALS and MS disease progression models. Research Outputs: Developed tools like AgreementMakerLight (AML) , KGsim-benchmark , and the Epidemiology Ontology . Over 133 publications with significant citations (32,909 reads, 3,889 citations). Teaching: Lectures advanced topics in Databases , Data Integration , Bioinformatics , and Big Data . Advocacy: Vice-president of Biodata.pt , promoting biological data valorization in Portugal. Actively involved in initiatives to promote computer science careers to young women .
