Raúl García-Castro is an Associate Professor at the Computer Science School of Universidad Politécnica de Madrid (UPM) , Spain. He works in the Ontology Engineering Group , focusing on semantic technologies, ontological engineering, and interoperability. His roles include Principal Investigator in EU projects like AURORAL , COGITO , and OntoCommons , and contributor to ETSI and W3C standards. Education : Ph.D. in Computer Science and Artificial Intelligence (UPM, 2008), M.Sc. in Computer Science (UPM, 2003). Research Interests center on benchmarking semantic technologies, ontological modeling, knowledge graph lifecycle, and semantic interoperability in IoT and blockchain. Recent work includes frameworks like LOT for industrial ontologies and WoTHive for Web of Things discovery. He has contributed to the SSN Ontology (W3C/OGC standard) and SAREF family of ontologies (ETSI standards). Scientific Awards include the UPM Extraordinary PhD Award (2008-2009) 3rd Best Paper Award at SEKE 2012 Grants include an FPI grant (BES-2005-8024) from Spain’s Ministry of Education. He has led projects like VICINITY (H2020-688467) and DELTA (H2020-773960), and participated in NeOn (FP6-027595) and Knowledge Web (FP6-507482). Leadership includes organizing conferences like SEKE 2021 (Chair), ESWC 2016 (Track Chair), and SEKE 2012 (Publicity Chair).
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نمایش ۷۶-۸۷ از ۸۷ استاد راهنما
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Professor Konstantinos Stefanidis holds a full professorship in Data Science at Tampere University's Faculty of Information Technology and Communication Sciences (ITC). He leads the Data Science Research Centre and Recommender Systems Research Group, focusing on advancing fairness and transparency in AI-driven systems. His work bridges computational techniques with societal impact, emphasizing ethical considerations in big data analytics. Education: PhD in Computer Science (2009) from University of Ioannina, Greece Postdoctoral research at institutions including Hong Kong, Norway, and Greece Research Interests: His primary areas include: Recommender Systems with fairness guarantees Entity Resolution in big data Algorithmic transparency and ethical AI Sequential group recommendations Health informatics applications He has published over 100 papers in top-tier venues like ICDE, EDBT, and VLDB. Recent Contributions: Recent work emphasizes: Counterfactual explanations for group recommendations Fairness-aware entity resolution Health domain recommendations Explainable AI frameworks Awards & Recognition: Best Paper Awards at ACM SAC (2022), PIC (2021), and QAUCA (2019) Recipient of Academy Research Fellowships General co-chair for major conferences (EDBT/ICDT 2026, ADBIS 2025) Advising & Collaboration: Mentors over 10 PhD/MSc students. Active in international collaborations including EU-funded projects. Leads research teams addressing global challenges like sustainable AI and healthcare innovation. Labs & Teams: Coordinates the Recommender Systems Group and Data Science Subunit at Tampere. Collaborates with industry partners and academic institutions globally to advance socially responsible technologies.
- Dataspaces
- Semantic Web Technologies
- Ontologies
- +۶ مورد دیگر
Johannes Theissen-Lipp is a senior scientific researcher at the Fraunhofer FIT Office Aachen with significant academic affiliations at RWTH Aachen University. He operates within the Department of Computer Science (Informatik 5) under the Faculty of Mathematics, Computer Science and Natural Sciences, working from the Databases and Information Systems (DBIS) group led by Professor Stefan Decker. His research focuses on semantic foundations of dataspaces with practical applications in manufacturing and mobility sectors. He earned his Computer Science degree from RWTH Aachen University, where he later conducted research at the Institute for Information Management in Mechanical Engineering (IMA) for nearly two years before joining the "Knowledge Pipelines" research group in December 2019. He successfully defended his PhD thesis titled "Semantic Foundations of Dataspaces" on September 25, 2024, completing his doctoral studies at RWTH Aachen. Theissen-Lipp's research spans semantic web technologies, ontologies, and data interoperability with particular expertise in RDF validation (SHACL), semantic mappings (RML), and FAIR data implementation within dataspaces. His work bridges theoretical foundations with industrial applications, especially in manufacturing contexts where data integration across organizational boundaries presents significant challenges. He has pioneered approaches for enhancing dataspaces through large language models and developing scalable validation techniques for distributed data environments. His publication record demonstrates consistent contributions to major semantic web venues including ESWC, SEMANTiCS, and dedicated workshops on dataspaces. As both author and editor (notably for the Second International Workshop on Semantics in Dataspaces proceedings), he has helped shape research directions in this emerging field. His recent work increasingly explores the intersection of traditional semantic technologies with contemporary AI approaches. Theissen-Lipp actively supervises multiple thesis projects including "FAIRification of Data Models in Manufacturing," "Leveraging Solid Pods for Sovereign Data Sharing in the Cultural Sector," and "Identification Techniques for Data-driven Feedback Loops in Manufacturing." He has taught the "Dataspaces Proseminar" in the summer semester of 2025, demonstrating his commitment to academic education alongside his research activities. As part of the Cluster of Excellence Internet of Production, he contributes to advancing data interoperability in industrial contexts. His professional presence extends to GitHub (JohannesLipp), LinkedIn, and ResearchGate, with a personal website providing additional details about his research portfolio and professional activities.
- Semantic Web
- Knowledge Graphs
- Linked Data
- +۳ مورد دیگر
Pieter Heyvaert is a Postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, Department of Electronics and information systems (EA06), affiliated with IMEC. His research focuses on semantic web technologies, knowledge graphs, and linked data systems. Heyvaert's research interests span several areas within computer engineering and semantic technologies: Semantic Web and Linked Data technologies Knowledge Graph generation and validation RDF mapping languages (particularly RML) Data integration from heterogeneous sources Sensor data processing and knowledge extraction Anomaly detection and root cause analysis His publication record demonstrates a consistent focus on practical applications of semantic technologies. Heyvaert has developed tools like the SAD Generator for creating knowledge graphs from academic event data and contributed significantly to RML-based systems. His work bridges theoretical semantic web concepts with practical implementations that address real-world data integration challenges across domains including healthcare, social media archiving, and IoT systems. Heyvaert has collaborated extensively with researchers in the semantic web community, particularly with Anastasia Dimou, Ruben Verborgh, and Ben De Meester. His recent work shows increasing application of semantic technologies to pressing societal issues like pandemic response through the COVID-19 literature knowledge graph.
Bram Steenwinckel is a postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. He holds an FWO Junior Postdoctoral Fellowship and contributes to IMEC research projects. His work combines knowledge graphs with machine learning for explainable hybrid AI applications. FWO-funded projects on anomaly detection and knowledge graph embeddings Co-author on 15+ publications in semantic web, AI, and healthcare informatics Research focuses on integrating domain knowledge into machine learning pipelines Involvement in both academic research and applied engineering solutions His research explores knowledge graph creation for smart monitoring systems across healthcare and industrial domains. The INK methodology enables semantic rule mining while TALK provides context-aware activity recognition . Current work extends these approaches to zero-shot classification and automotive quality control . Scientific contributions include: Explainable knowledge graph embeddings Context-aware IoT data stream analysis Hybrid AI for ambulatory health monitoring Dynamic dashboarding architectures
Samira Si-Said Cherfi is a Professor at the Centre d'études et de recherche en informatique et communications (CEDRIC) within the Conservatoire National des Arts et Métiers (CNAM) in Paris. With a research career spanning over 25 years, she has established herself as a leading expert in data quality, conceptual modeling, and ontology engineering. Her work bridges theoretical computer science with practical applications in healthcare systems, business process management, and knowledge representation. Her research interests focus on data quality assessment , conceptual modeling methodologies , ontology engineering for complex systems , and cyber-physical security in healthcare infrastructures . She has pioneered approaches for evaluating RDF data completeness, developing quality metrics for conceptual schemas, and creating ontologies for healthcare security. Her work demonstrates how formal modeling techniques can solve real-world problems in information systems. Analysis of her recent publications reveals a strong trend toward cyber-physical security and healthcare information systems , where she applies semantic technologies to address cascading effects in critical infrastructures. Her work consistently connects theoretical foundations in conceptual modeling with practical applications in knowledge graphs and data integration. She has made significant contributions to understanding how OWL semantics can be effectively utilized in RDF-based knowledge graphs. As an active member of the academic community, she has served as guest editor for special journal issues and contributed to major international conferences including RCIS, CAiSE, and EDOC. Her leadership in the field is evident through her editorial roles and collaborative research projects. Professor Si-Said Cherfi leads research within the CEDRIC laboratory, specifically contributing to the 'Complex data, machine learning and representations' and 'Data mining and statistics' research teams. Her work often involves interdisciplinary collaboration with healthcare professionals, security experts, and industry partners to address complex challenges in information systems security and data quality.
Nicolas HIOTمشاهده پروفایل
پژوهشگر ارشد
Nicolas HIOT is a Post-doctoral fellow at the University of Orleans affiliated with the LIFO laboratory (Laboratoire d'Informatique Fondamentale d'Orléans) and the Pamda project. His research bridges database systems, natural language processing, and medical informatics with a focus on text-to-database integration and consistency maintenance. His research interests center on: Database Systems for medical applications with emphasis on consistency and evolution Natural Language Processing for clinical text analysis and relation extraction Knowledge Graph construction from unstructured textual data Medical Informatics applications for healthcare data management Analysis of his 15 most recent publications (2020-2024) reveals a cohesive research trajectory at the intersection of databases and NLP. Key thematic clusters include automated medical database construction from clinical texts, consistency management in evolving RDF/property graph systems, and clinical entity/relation extraction for knowledge graphs. His work consistently addresses real-world challenges in healthcare data integration through tools like DataFix and ArchiTXT, demonstrating strong translational potential. Nicolas HIOT actively contributes to the LIFO research laboratory at the University of Orleans, collaborating extensively with Jacques CHABIN, Mirian HALFELD-FERRARI, and Dominique LAURENT. His technical output includes multiple software systems for database evolution management and clinical text processing, reflecting both theoretical contributions and practical implementations in semantic data management.
Andreas Both is a Professor at the Faculty of Computer Science and Media at Leipzig University of Applied Sciences (HTWK Leipzig), where he leads the Web & Software Engineering (WSE) research group. His work spans multiple domains of computer science with a strong focus on bridging theoretical foundations with practical applications in software engineering, web technologies, and artificial intelligence. His research interests primarily revolve around Software Engineering (particularly test automation with AI and source code analysis), Web Engineering , Applied Artificial Intelligence (including Machine Learning, Deep Learning, and Large Language Models), Question Answering & Chatbots , and Data-driven Applications . He has developed innovative approaches in knowledge graph question answering systems, multilingual NLP applications, and privacy-preserving data sharing technologies using the Solid protocol framework. The analysis of his recent publications reveals a strong trajectory toward leveraging Large Language Models for knowledge graph applications, with particular emphasis on multilingual capabilities, explainability, and quality improvement in question answering systems. His work increasingly integrates privacy considerations with advanced AI techniques, particularly through Solid protocol implementations for data sovereignty. Best Paper Award at ICWE 2024 for AuthApp - a GDPR-compliant access granting system Outstanding Paper Award at ICWI 2024 for LLM-generated explanations in question answering systems Multiple first-place awards at the TEXT2SPARQL Challenge 2025 Best Paper Awards at ICWE 2025 and IEEE ISI 2025 CHI 2015 Honorable Mentions for search interface research Professor Both actively mentors students through the Google Summer of Code program and serves on the leadership board of the Architecture (ARC) working group of the German Computer Science Society (Gesellschaft für Informatik). His teaching portfolio includes Software Engineering, Question Answering & Chatbots, Software Projects, Project Management Practicum, Web Engineering, and Software Engineering & AI courses. His office hours are Thursdays from 11:15-12:15, requiring advance email appointment with topic specification.
Maurizio Lenzerini is a Full Professor at the Department of Computer, Control and Management Engineering Antonio Ruberti , Sapienza University of Rome. He is a leading international expert in Ontology-Based Data Management , Description Logics , and Data Integration , with foundational contributions to Semantic Web technologies and service composition. ACM Fellow (2009) AAAI Fellow (2021) Peter P. Chen Award (2022) ACM Recognition of Service Award (2008) His research spans Artificial Intelligence , Database Theory , and Service-Oriented Computing , focusing on inference mechanisms, query rewriting, and knowledge graph ecosystems. He has pioneered MASTRO , a tool for ontology-based data access, and led European projects in data interoperability. Recent publications highlight advancements in knowledge graph lifecycle management , semantic classifier explanations , and quality-aware data integration . As PODS 2024 Executive Committee Chair , he shapes database theory research agendas. Teaching includes courses on Databases , Data Management , and Logic in Computer Science , with materials on SQL, ER modeling, and relational algebra.
Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.
Sussi Olsenمشاهده پروفایل
پژوهشگر
Sussi Olsen is an Academic Research Staff member (Researcher) in the Department of Nordic Studies and Linguistics at the University of Copenhagen, employed at the Center for Sprogteknologi (CST) since 1997. Her work centers on developing and validating linguistic resources for language technology applications. Education: Masters degree in Spanish and Computational Linguistics, University of Copenhagen Her research spans technological language resources, morphological/syntactic/semantic annotation, computational lexicography, corpus linguistics, and resource validation. She specializes in Danish language processing and EU-level language technology infrastructure, with expertise in historical Danish (19th century) and multilingual resource integration. Her work bridges theoretical linguistics and practical NLP applications. Recent publications (2023-2025) reveal a strategic focus on semantic resource development for Danish, including formal lexicons (COR.SEM), language model evaluation frameworks, and sign language-spoken language integration. These works demonstrate convergence between lexical semantics, benchmarking methodologies, and European language equality initiatives, particularly through EU-funded projects like the European Language Equality agenda. She actively contributes to EU language technology projects including the Central Word Register for Danish (COR), European Language Grid, Federated TermBank, and ELEXIS. Her project portfolio shows sustained engagement with terminology management, semantic annotation standards, and resource validation since 2008. She has presented extensively on historical Danish language processing and semantic annotation methodologies. Olsen is affiliated with the Center for Sprogteknologi and participates in the DigHumLab project. She holds governance roles as Member of the Board of Representatives and Board of Directors of the Danish Language Council, and formerly chaired the Society of Lexicographers in Denmark.
Brice Chardinمشاهده پروفایل
دانشیار
Brice Chardin is an Associate Professor in Data Engineering at ISAE-ENSMA since 2013, affiliated with the LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) Data and Model Engineering team. His work bridges academic research and industrial applications, focusing on data management solutions for critical systems. His research spans clustering algorithms under dissimilarity constraints , RDF query relaxation for explaining empty/overabundant results, pattern mining through the RQL language, and energy data management . Key projects include Chronos (a NoSQL system for industrial sensor data) and collaborations with energy companies SRD and Nexeya for predictive consumption analysis. Recent publications (2021-2024) emphasize constrained clustering techniques and cooperative query processing for RDF knowledge bases, revealing a strong trend toward practical solutions for industrial data challenges. His work integrates machine learning with database theory to address real-world data imperfections. PhD in Computer Science from INSA Lyon (2011) Postdoctoral position at LIRIS (2012-2013) on ANR DAG project Specialized in industrial data management since 2011 EDF collaboration Chardin actively supervises academic projects including drone simulation with Ardupilot and Smart Data mining initiatives. His industrial partnerships focus on energy sector applications, particularly predictive analysis for electricity distribution and storage systems. Current work involves developing clustering algorithms with error bounds and query relaxation frameworks for semantic web technologies.