Daniel Hernández is a Postdoctoral Researcher at the Institute for Artificial Intelligence (KI) under the Cluster of Excellence IntCDC at the University of Stuttgart. He is part of the Analytic Computing group within the Institute for Parallel and Distributed Systems (IPVS). His work focuses on Semantic Web technologies , particularly SPARQL , RDF , and knowledge graph applications in interdisciplinary design workflows . Research Trends : His publications (2015–2025) emphasize semantic query processing , provenance computation , and interoperability between architectural data and knowledge graphs . Key innovations include the eSPARQL language for epistemic queries, NPCS for native provenance in SPARQL, and BHoM to bhOWL for integrating building data with ontologies. Teaching & Collaborations : He has held teaching roles at the University of Stuttgart ( Human-Computer Interaction with Knowledge Graphs ), University of Aalborg ( Group Supervisor ), and University of Chile ( Lecturer for The Web of Data ). Collaborations span institutions like Buro Happold , TU Wien , and INRIA , with publications in journals like Proceedings of the VLDB Endowment and conferences such as WWW and ISWC .
Dr. Amal Zouaq is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. She holds the FRQS (Dual) Chair in AI and Digital Health, serves as Director of the LAMA-WeST research laboratory, and is an Associate Member of MILA. Her work bridges artificial intelligence with applications in digital health, cultural heritage, and educational technologies, positioning her at the forefront of interdisciplinary AI research in Canada. Her research focuses on Artificial Intelligence , particularly Natural Language Processing and the Semantic Web . Specific interests include knowledge representation, ontology learning, SPARQL query generation, bias mitigation in language models, and clinical text processing. Her work spans multiple domains including healthcare, cultural heritage, and educational technology, with emphasis on developing practical AI solutions that address real-world challenges in knowledge management and information extraction. Analysis of her recent publications reveals a strong trajectory in advancing NLP techniques for knowledge-intensive applications. Her work increasingly focuses on domain-specific applications in healthcare and cultural heritage, with growing emphasis on ethical AI considerations like bias mitigation. The research demonstrates progression from foundational semantic web technologies toward more sophisticated neural approaches while maintaining strong theoretical grounding in knowledge representation. Scientific Recognition: Holder of the FRQS (Dual) Chair in AI and Digital Health Dr. Zouaq has supervised 23 graduate students to completion, including 1 PhD and 22 Master's theses, with research spanning ontology learning, knowledge representation, and NLP applications. Her supervision record demonstrates consistent mentorship in cutting-edge AI research with practical applications across multiple domains. She actively serves on program committees for major conferences in knowledge engineering, data mining, and semantic web technologies. She directs the LAMA-WeST (Web, Semantics and Text) laboratory , which specializes in natural language processing and artificial intelligence research. The lab focuses on knowledge representation, semantic technologies, and their applications in healthcare, cultural heritage, and educational contexts. As a member of IVADO and MILA, she collaborates with leading AI researchers across Montreal's vibrant AI ecosystem.
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.
Axel Polleres is a full professor at the Institute for Data, Process and Knowledge Management in Vienna University of Economics and Business (WU Wien). He leads the department of Information Systems and Operations Management while maintaining active research in knowledge graphs, semantic web technologies, and ontology engineering. PhD and Habilitation from Vienna University of Technology Former positions at University of Innsbruck, Universidad Rey Juan Carlos, DERI Ireland, and Siemens AG Co-chair of W3C SPARQL working group Editorial board member for Semantic Web Journal and IJSWIS His research focuses on: Querying and reasoning over ontologies Graph schema languages (SHACL, SPARQL) Wikidata constraint formalization Ontology reuse in collaborative platforms Crisis management knowledge graphs FAIR data principles implementation Recent publications analyze knowledge graph evolution, constraint validation methodologies, and semantic web standardization efforts. Key topics include: OWL/RDF interoperability solutions Unit conversion systems for Wikidata Partition-based query processing frameworks Network resilience analysis for urban planning Open data platform discovery tools Temporal analysis of collaborative knowledge graphs He has co-organized major conferences like ISWC2023 and ESWC workshops while maintaining active roles in European research projects. Current work involves spatiotemporal knowledge graphs for city resilience and semantic web infrastructure development.
Michalis Mountantonakis is a Postdoctoral Researcher at FORTH and Laboratory Teaching Staff in the Department of Computer Science at the University of Crete, Greece. He holds a PhD (2020), MSc (2016), and BSc (2014) in Computer Science from the University of Crete, all with top grades. His research focuses on Large-Scale Semantic Data Integration, Linked Open Data, and Semantic Web technologies, with over 45 publications in top venues like ACM VLDB, ISWC, and ECML. He has been awarded the prestigious SWSA Distinguished Dissertation Award (2020) and the Maria Michael Manasaki Fellowship (2020). His work includes tools like LODsyndesis and LODChain, addressing challenges in knowledge graph connectivity and validation of AI-generated content. Education: PhD in Computer Science (2016-2020), University of Crete (Excellent GPA 9.74/10) MSc in Computer Science (2014-2016), University of Crete (Excellent GPA 9.87/10) BSc in Computer Science (2010-2014), University of Crete (2nd in class with GPA 8.42/10) Research Interests: His work bridges semantic web technologies with modern AI challenges, emphasizing large-scale data integration, knowledge graph applications, and validation frameworks. He has contributed to cultural heritage informatics, machine learning-augmented semantic systems, and cross-lingual NLP solutions. Recent trends include leveraging LLMs for query generation and semantic enrichment while ensuring factual accuracy through knowledge graph-driven validation. Key Achievements: Developed LODsyndesis, a global-scale semantic integration service Pioneered real-time validation of ChatGPT responses using RDF knowledge graphs Won Best Paper Award (ISWC 2022) for entity enrichment techniques Recipient of Stelios Orphanoudakis Undergraduate Fellowship (2013-2014) Participated in Roche Continents 2019 (top 100 European science students) Grants & Labs: His research has been supported by GSRT/HFRI. He collaborates with FORTH-ICS and leads projects in EU-funded initiatives like iMarine and BlueBridge. Current work focuses on governance models for ontologies, interoperable thesaurus creation (e.g., FoodEx2), and semantic analytics for cultural heritage datasets.
Olaf Hartig is a Senior Associate Professor at Linköping University's Department of Computer and Information Science (IDA), affiliated with the Database and Information Techniques (ADIT) division. He is also an Amazon Scholar collaborating with the Neptune graph database team. His research focuses on data management, semantic web technologies, graph databases, and distributed data systems. Hartig holds a PhD from Humboldt-Universität zu Berlin and is a Docent at Linköping University. He has received numerous awards, including the SWSA Distinguished Dissertation Award and eight best paper awards, and was selected as a Wallenberg Academy Fellow in 2024. Education: PhD in Computer Science (Humboldt-Universität zu Berlin), Docent (Linköping University). Research interests span query processing for Linked Data, federated systems, RDF and GraphQL semantics, and knowledge graph construction. He leads research groups in Database and Web Information Systems and Semantic Web Technologies at IDA. Key achievements include pioneering traversal-based query execution, developing Triple Pattern Fragments, and contributions to standards like RDF* and SPARQL*. His work has been recognized through grants, patents (e.g., on graph acceleration techniques), and leadership roles in conferences like ISWC and ESWC. Teaching: Course leader for database technology courses (TDDD12, TDDD37) and advanced topics like big data analytics and bioinformatics databases. Active in curriculum design and interdisciplinary education. Labs/Teams: Database and Web Information Systems Group, Semantic Web Research Group, Sports Analytics Group (IDA) Grants: Wallenberg Academy Fellowship, Swedish Research Council funding
Juan F. Sequeda is the Principal Scientist and Head of the AI Lab at data.world, with a PhD in Computer Science from the University of Texas at Austin. He co-founded Capsenta, a spin-off from his research on semantic data virtualization. His work bridges academia and industry through roles in the Property Graph Schema Working Group, LDBC Graph Query Languages task force, and W3C standards editing. Education : PhD in Computer Science (2015) from University of Texas at Austin His research focuses on Knowledge Graphs, Semantic Web, and Ontology-Based Data Integration. He develops technologies for graph data management and semantic query processing, with applications in constitutional data analysis (Constitute.org) and enterprise data virtualization (Ultrawrap, Gra.fo, G-CORE). Recent publications analyze composable graph query languages (G-CORE, 2018) and optimize SPARQL execution on relational data (Ultrawrap, 2013). Awards include NSF Graduate Fellowship (2010-2013), ISWC2014 Best Student Paper, and 2015 Institute for Applied Informatics Best Transfer Project. Scientific Awards : NSF Graduate Research Fellowship (2010-2013) 2nd Place, 2013 Semantic Web Challenge Best Student Research Paper, ISWC2014 2015 Best Transfer and Innovation Project (Institute for Applied Informatics) UT Graduate Diversity Fellowship (2008-2009) National Instruments Scholarship (2007-2008) Intel Foundation Fellowship (2007) He actively contributes to program committees (ISWC, ESWC, WWW) and workshop organization (COLD, AMW2018 General Chair). Contact: juan@data.world (work), juanfederico@gmail.com (personal).
Sebastián Ferrada is an Assistant Professor at the Data & Artificial Intelligence Initiative of Universidad de Chile. He also serves as Young Researcher at the Institute for Foundational Research on Data (IMFD) and Collaborating Researcher at the National Center for Artificial Intelligence Research (CENIA). His research focuses on Knowledge Graphs, with special emphasis on extraction, management, and applications for querying, browsing, and AI systems. His academic background includes: PhD in Computer Science (2021), Universidad de Chile MSc in Computer Science (2017), Universidad de Chile BEng in Computer Science (2017), Universidad de Chile Sebastián's research explores several key areas: Multimedia Databases with applications to Wikimedia Commons images Graph Databases and Knowledge Graphs construction Federated Data Management across heterogeneous RDF sources SPARQL query extensions for similarity-based operations Graph data management and compression techniques His recent publications demonstrate strong trends in knowledge graph construction, similarity-based querying, and efficient graph data management. These works combine theoretical advancements with practical implementations in real-world systems like IMGpedia and MillenniumDB. Scientific achievements include: Best Paper Award at CoopIS 2023 Best Demonstration Award runner-up at SIGMOD/PODS 2024 Best Student Paper (Resources Track) and Best Poster at ISWC 2017 First prize in CLEI 2017 for his Master's thesis Sebastián currently leads the Fondecyt project on graph data management and contributes to the U-Inicia project on AI processes in graph databases. He serves on the editorial board of Transactions on Graph Data and Knowledge.
Katja Hose is a Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Her research focuses on Data, Knowledge and Web Engineering with specializations in AI for the People and Artificial Intelligence and Machine Learning. She maintains an active research profile with numerous publications and projects. Department of Computer Science Technical Faculty of IT and Design Aalborg University Research areas: Query Processing, Semantic Web, Linked Data, Knowledge Graphs Professor Hose's research interests center on knowledge representation, semantic web technologies, and AI applications. Her work spans from theoretical database systems to practical applications in healthcare, environmental assessment, and microbial data analysis. She has made significant contributions to knowledge graphs, large language models, and semantic search technologies, with particular emphasis on addressing hallucinations in AI systems and improving table search in semantic data lakes. Her recent publications demonstrate a strong trend toward integrating knowledge graphs with large language models, developing evaluation frameworks for AI hallucinations, and applying data science to diverse domains including healthcare and environmental sustainability. Her research bridges theoretical computer science with practical applications that address real-world challenges. NLP4KGC Best Paper Award (2023) ESWC 2023 Best Demo Award (2023) 2020 AMiner AI 2000 Most Influential Scholars AIME 2020 Best Paper Nomination (2020) ESWC 2019 Best Demo Award Nomination (2019) Professor Hose leads multiple significant research projects including ARISTOTLE (AI for clinical risk assessment), DarkScience (microbial data analysis), and the Poul Due Jensen Professorate in Big Data and AI. She has supervised numerous PhD students and collaborates extensively across disciplines, particularly in healthcare applications of AI and environmental assessment technologies. Her research has attracted substantial funding from sources like Villum Fonden and Danish E-infrastructure Cooperation. She is actively involved in several interdisciplinary research teams, including collaborations with microbiologists on microbial dark matter projects and with environmental scientists on digital environmental assessment systems. Her work on the ARISTOTLE project demonstrates strong connections between AI research and clinical applications, while her DarkScience project bridges computer science with microbiology.
Georg Gottlob is a Professor at the University of Oxford's Department of Computer Science, with additional affiliation at TU Vienna's Faculty of Informatics. He has maintained an exceptionally productive research career spanning over four decades, with 494 publications documented in the DBLP database from 1983 to the present. His research interests focus on Database Theory , Logic Programming , and Knowledge Graphs , with particular expertise in hypertree decompositions, Datalog systems, and existential rules. His work bridges theoretical foundations with practical applications, as evidenced by his development of the Vadalog system for knowledge graph reasoning. Gottlob's recent publications (2023-2025) demonstrate continued innovation in query optimization, rule-based reasoning, and the integration of large language models with database systems. His work shows a consistent trend toward making theoretical advances in database theory practically applicable, particularly in the context of knowledge graphs and semantic web technologies. Scientific Awards: 2020 ACM PODS Alberto O. Mendelzon Test-of-Time Award for influential contributions to database theory Gottlob maintains extensive research collaborations with scholars including Reinhard Pichler, Andreas Pieris, and Matthias Lanzinger. His work has significant practical impact through systems like Vadalog, which combines machine learning with logical reasoning for knowledge graph applications. He has supervised numerous PhD students (though specific names aren't listed in the DBLP record) and has been instrumental in advancing the field of database theory from theoretical foundations to real-world applications. His research group focuses on the intersection of database theory, knowledge representation, and artificial intelligence, with particular emphasis on developing efficient algorithms for complex query processing and reasoning tasks over large knowledge graphs.
Ruben Taelman is a postdoctoral researcher at IDLab , Ghent University – imec, specializing in decentralization, Linked Data publishing, and querying. He develops open-source JavaScript libraries like the Comunica engine for decentralized data access and focuses on intelligent infrastructure for data publication and retrieval. His research bridges academic and industrial perspectives, addressing challenges in decentralized knowledge graphs, policy negotiation, and data governance. Key projects include Triple Storage for versioned RDF querying and contributions to the Solid ecosystem for user-centric data control.
Kenza Kellou-Menouer is a researcher affiliated with the ETIS Laboratory at ENSEA, France, and part of the MIDI research group . Her work focuses on schema discovery for Semantic Web data, data mining, and big data optimization. Research: Semantic schema discovery, clustering/classification algorithms, and association rules. Teaching: Semantic Web technologies, database design, algorithms, and programming languages (Java, C++, C#, C). Research Interests center on Semantic Web data integration, RDF schema inference, and hybrid machine learning approaches. She has contributed to scalable schema discovery systems and real-time profiling techniques for large datasets. Publications include work on schema inference tools (SchemaDecrypt++, HInT) and methodological frameworks presented at top-tier venues like VLDB (A*), ICDE (A*), SSDBM (A), and ISWC . Her research bridges theoretical advancements with practical implementations for RDF datasets. Community Contributions include organizing tutorials at the International Semantic Web Conference (ISWC) 2022 and developing educational materials for database and programming courses.
Mariano Rico is an Associate Professor at the Polytechnic University of Madrid (UPM), affiliated with the OEG research group in the Artificial Intelligence Department. Previously, he served as a Senior Researcher at OEG (2016-2020) and held teaching roles at the Autonomous University of Madrid (UAM). His primary affiliations include the UPM's Faculty of Computer Science and the UAM's Computer Engineering Department. Education: PhD in Computer Science (UAM, 2009), MSc in Physics (UAM, 1992), and postgraduate studies in Telecommunications Engineering. He conducted research stays at DERI (Ireland) and Freie Universität Berlin, focusing on Semantic Web and Linked Data. Research interests center on Linked Open Data, Natural Language Processing (NLP), and Semantic Web technologies, with contributions to DBpedia's Spanish branch and projects like Wf4Ever and LIDER. He actively collaborates with institutions in Leipzig, Bielefeld, and Berlin on Linked Data and linguistic applications. Teaching: Coordinates courses in NLP, Linguistic Engineering, and Big Data Visualization at UPM and online programs. Has instructed over 300 UAM faculty through teacher training programs on LaTeX, bibliographic management, and digital scholarly practices. Projects: Lead roles in European and national initiatives including SlideWiki, UpGrid, and Neptune. Current work focuses on NLP applications like text summarization (esT5s) and terminology tools (TermInteract). Labs/Teams: Core member of the OEG group, contributing to semantic web infrastructure and NLP tool development. Maintains international collaborations through AKSW and CITEC groups.
Pasquale Lisena is a Research Fellow in the Data Science department at EURECOM, where he contributes to the Data2Knowledge research group. His work bridges academic research with practical applications in knowledge-intensive domains. Education: PhD in Computer Science from EURECOM / Sorbonne University (2019), thesis: "Knowledge-based music recommendation: Models, algorithms and exploratory search" supervised by Raphaël Troncy His research centers on Knowledge Graphs, Knowledge Engineering, and Recommender Systems with strong applications in cultural heritage and music. Key specializations include: Semantic Web technologies for domain-specific knowledge representation Information extraction from multimedia and text sources Development of ontologies for complex cultural domains Music metadata modeling and recommendation systems Recent publications reveal a trajectory from foundational music metadata work (2018-2019) toward cutting-edge applications in olfactory heritage (Odeuropa) and language model-driven recommendation (2025). His research consistently integrates knowledge graphs with machine learning, demonstrating expertise in both classical music informatics and emerging sensory heritage domains. Awards: Best Resource Paper Award (2022) for Odeuropa Data Model publication Funding and Collaboration: ANR JCJC project coordinator for kFLOW (2022-2024) Key contributor to EU H2020 projects including Odeuropa, SILKNOW, and MeMAD Extensive cross-institutional collaboration across European cultural heritage initiatives He operates within EURECOM's Data2Knowledge group, participating in international research consortia focused on knowledge-intensive applications in cultural heritage preservation and digital humanities.
Catherine Faron is a Full Professor at Université Côte d'Azur , affiliated with the I3S laboratory and Inria center . She serves as vice-head of the Wimmics joint research team and leads the Artificial Intelligence and Data Engineering (IAID) program at Polytech Nice Sophia engineer school. Habilitation à diriger les recherches (HDR) in Computer Science, UCA (2017) PhD in Computer Science, Univ. Paris 6 (1997) Her research focuses on Artificial Intelligence , particularly in Knowledge Representation and Reasoning (KRR) and Semantic Web technologies. She develops hybrid intelligent systems combining KRR with machine learning for knowledge extraction, integration, and exploitation across education, health, and digital humanities. Recent publications highlight her work on: 2025 : Knowledge graphs for historical zoological data 2024 : Semantic annotation frameworks in agronomy 2023 : Agricultural data mapping and medical record enrichment 2022 : Visual exploration of big linked data Scientific recognitions include: 2023: Best Paper Award, ESWC 2017: Scientific Excellence Award, UCA 2016: Best Demo Award, ISWC 2015: Best Paper Award, IC 2008: Best PhD Paper Award, ECPPM She has supervised 19 PhD/Master's students and leads/has led projects like D2KAB , DEKALOG , and ZOOMATHIA , with partnerships across academic and industrial institutions.
