Find Academic Supervisors

• Data Science
• Artificial Intelligence
• Knowledge Engineering
• +9 more

Christoph Lange-Bever (publishing as Christoph Lange) is a senior researcher in computer science, specializing in data science and artificial intelligence, affiliated with the Fraunhofer Institute for Applied Information Technology FIT and the Chair for Information Systems at RWTH Aachen University since 2019. He leads the Data Science and Artificial Intelligence department at Fraunhofer FIT and previously headed the Knowledge Pipelines department from 2019–2021. His career spans roles at the University of Bonn, Fraunhofer IAIS, University of Birmingham (as an honorary research fellow until 2017), and University of Bremen. PhD from Jacobs University Bremen (2011) on semiformal mathematical knowledge collaboration Focus areas include knowledge engineering, semantic web, data infrastructures , and FAIR principles Applications span enterprise data integration , research infrastructures , and cross-organization data spaces His recent publications (2023–2025) emphasize FAIR data spaces , LLM integration , semantic interoperability , and knowledge graph tools . While no specific scientific awards are listed, he has co-supervised 10 PhD students and contributed to international standards (OMG, W3C). He teaches courses like Knowledge Graph Lab and Sovereign Data Exchange , blending research with practical applications for global enterprises.

• Semantic Data Integration
• Ontology-based Database Design
• Big Data & Cloud Computing
• +5 more

Ladjel Bellatreche is a Full Professor at the National Engineering School for Mechanics and Aerotechnics (ISAE-ENSMA) in Poitiers, France, where he has been a faculty member since September 2010. He leads the Data and Model Engineering Team of the Laboratory of Computer Science and Automatic Control for Systems (LIAS). Prior to his current position, he spent eight years as Assistant and then Associate Professor at Poitiers University. His academic journey includes visiting positions at Université du Québec en Outaouais (Canada), Purdue University (USA), and Hong Kong University of Science and Technology (China). Professor Bellatreche's research interests span multiple domains in data management and engineering, with a focus on Semantic Data Integration, Ontology-based Database Design, Life Cycle of Extremely Large Database Design, Big Data & Cloud Computing, Green Computing, and Database Deployment. His work bridges theoretical foundations with practical applications in data-intensive systems. His publications reflect a strong trend toward addressing challenges in big data analytics, semantic data integration, and energy-efficient database systems. The research demonstrates a progression from traditional data warehousing techniques to more advanced approaches incorporating semantic web technologies, linked open data, and green computing principles. His work increasingly focuses on scalability, efficiency, and the integration of diverse data sources. Professor Bellatreche has received recognition through his service as an Editorial Board Member for the International Journal of Reasoning-based Intelligent Systems and as subject area editor of the Scalable Computing Journal. He has also served as a reviewer for prestigious journals including IEEE TKDE and Distributed and Parallel Database Journal. His leadership extends to organizing major international conferences such as DAWAK, DOLAP, MEDI, and WISE. With over forty program committee memberships, he plays a significant role in shaping research directions in data management. Additionally, he actively promotes research in Africa and Asia through student supervision and conference organization.

Andrea Giovanni NuzzoleseView profile

Researcher

• Knowledge Extraction
• Ontology Design Patterns
• Linked Data
• +4 more

Andrea Giovanni Nuzzolese is a Researcher at the Semantic Technology Laboratory (STLab) of the National Research Council (CNR) in Rome, Italy. He earned his PhD in Computer Science from the University of Bologna in 2014. His core research focuses on Knowledge Extraction , Ontology Design Patterns , Linked Data , and Semantic Web technologies. Education: PhD in Computer Science (2014), University of Bologna Research Focus: Ontology engineering, semantic AI, and knowledge graph applications in cultural heritage and healthcare Projects: EU-funded IKS project, Apache Stanbol developer, and contributions to Italian Cultural Heritage (ArCo) and healthcare (Geriatric Assessment) ontologies His recent publications emphasize hybrid AI for global governance, LLM-driven ontology generation, and semantic infrastructure development. While no formal awards are listed, his work is recognized in international journals and conferences.

• Natural Language Processing
• Information Retrieval
• Web Personalization
• +6 more

Wolfgang Nejdl is a distinguished Professor at Leibniz University Hannover, working within the Faculty of Mathematics and Computer Science and affiliated with the Institute of Information Systems. With an extensive publication record spanning over two decades, he has established himself as a leading researcher in Natural Language Processing, Information Retrieval, and Web Personalization. His work bridges theoretical advances with practical applications, particularly in clinical NLP and semantic web technologies. Nejdl's research interests encompass a broad spectrum of topics including Natural Language Processing, Information Retrieval, Web Personalization, Semantic Web technologies, Machine Learning applications, Clinical NLP, and Recommender Systems. His recent work has focused on critical challenges such as clinical outcome prediction using MIMIC datasets, financial literacy evaluation of large language models, and developing resources for low-resource languages like Tigrinya. He has made significant contributions to understanding data drift in clinical applications and developing interpretable AI systems for healthcare. His publication record shows a consistent trajectory of impactful research, with recent articles demonstrating his ability to tackle emerging challenges in NLP and AI. From his foundational work on web personalization using ODP metadata to his current research on clinical NLP and low-resource language processing, Nejdl has consistently addressed important problems at the intersection of information systems and human needs. His work on stance detection incorporating toxicity and morality analysis represents innovative approaches to understanding social media discourse. Among his notable achievements are the development of the EDUTELLA P2P infrastructure based on RDF, significant contributions to boilerplate detection algorithms, and pioneering work on preventing shilling attacks in recommender systems. His research has been widely cited, reflecting its substantial impact on the fields of web science and natural language processing. Nejdl has supervised numerous students and collaborated extensively with researchers worldwide, particularly with Alexander Loeser, Jens-Michalis Papaioannou, and Paul Grundmann. His work demonstrates a consistent focus on practical applications of theoretical advances, particularly in healthcare and web technologies. He has also worked on important infrastructure projects like the L3S Research Center, contributing to the broader academic and technological ecosystem.

Varish MulwadView profile

Researcher

• Information Extraction
• Knowledge Graph Population
• Semantic Web
• +4 more

Varish Mulwad is a Senior Scientist at GE Research with 14 years of experience in algorithm development and knowledge graph construction from structured/unstructured data. He holds a Ph.D. and M.S. in Computer Science from the University of Maryland, Baltimore County (UMBC), where he worked under Prof. Tim Finin and collaborated with Prof. Anupam Joshi, and a B.E. in Computer Engineering from University of Mumbai. Ph.D. & M.S. in Computer Science (UMBC) B.E. in Computer Engineering (University of Mumbai) His research focuses on semantic interpretation of tabular data through linked data frameworks, information extraction from unstructured text, and knowledge graph population using probabilistic reasoning and graphical models. He has pioneered domain-independent systems for table interpretation and developed novel methods for cloud SLA automation and cybersecurity threat detection via social media analysis. Recent work trends include: Context-aware web table annotation Relational table representation learning Linked data generation from spreadsheets Pre-training/fine-tuning paradigms for web tables Application of semantic web standards to cybersecurity He has led 3-4 member project teams in developing production-ready solutions, contributed to 19 peer-reviewed publications, and secured 7 patents with 900+ citations. During his academic tenure at UMBC's Ebiquity Research Lab, he co-developed the TABEL framework for table semantics inference and produced the first interactive system for meta-analysis report generation from linked data.

Andreas BothView profile

Professor

• Software Engineering
• Web Engineering
• Artificial Intelligence
• +7 more

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.

Basil EllView profile

Researcher

• Semantic Web
• Natural Language Processing
• Knowledge Base Population
• +5 more

Basil Ell is a Researcher at the University of Oslo's Department of Data and Knowledge Management, affiliated with SIRIUS labs. He holds a PhD from Karlsruhe Institute of Technology (KIT) and has held postdoctoral positions at Bielefeld University's CITEC. His work bridges Semantic Web technologies and Natural Language Processing, focusing on knowledge base population, semantic parsing, and information extraction from text/tables. He splits his time between Oslo and Bielefeld, contributing to projects like semantically enhanced Virtual Research Environments and table understanding systems. Education: B.Sc./M.Sc. in Computer Science from Mannheim University of Applied Sciences, Pohang University of Science and Technology, and IIT Madras. PhD (2015) from KIT under Prof. Rudi Studer. Research Trends: Recent publications emphasize semantic data integration (e.g., materials informatics), link prediction in knowledge graphs, and clinical trial evidence synthesis. His work often combines RDF data structures with NLP techniques. Labs/Teams: Active in SIRIUS labs (Oslo) and previously at CITEC/Bielefeld's Semantic Computing group.

• Knowledge Engineering
• Semantic Web
• Data Infrastructures
• +7 more

Christoph Lange is the Head of the Data Science and Artificial Intelligence department at the Fraunhofer Institute for Applied Information Technology (FIT) and a senior researcher at the Chair of Information Systems 5 (DBIS) at RWTH Aachen University, Germany. He has been affiliated with RWTH Aachen since 2019 and with Fraunhofer since 2014. He received his Ph.D. from Jacobs University Bremen in 2011 for work on enabling collaboration on semiformal mathematical knowledge through semantic web integration. His research focuses on knowledge engineering, data infrastructures, and semantic technologies, particularly aligned with Linked Data and FAIR principles (Findable, Accessible, Interoperable, Reusable). Applications span enterprise data integration, research infrastructures, and cross-organizational data spaces, with contractual R&D conducted for leading German and international automotive and industrial enterprises. His research interests include Knowledge Engineering , Semantic Web , Knowledge Graphs , Ontology Engineering , FAIR Data , and Intelligent Computer Mathematics . He has co-supervised 10 PhD students and taught courses such as the Knowledge Graph Lab at RWTH Aachen. He has organized numerous conferences, workshops, and training events in semantic web and intelligent mathematics domains and contributed to W3C and OMG international standards. Recent publications (2023–2025) demonstrate a strong trend towards integrating Large Language Models with semantic technologies for FAIR data spaces, ontology development, and explainable AI. His work also advances tools for knowledge graph creation (e.g., KGraphX), RDF validation, federated data catalogues, and semantic interoperability in manufacturing and scientific domains. h-index 30 (Google Scholar) Christoph Lange has served as an expert reviewer for international funding bodies and has led impactful collaborations across academia and industry. His work bridges theoretical advances in knowledge representation with real-world applications in data-intensive sectors. He is actively involved in research projects such as NFDI4DS and FAIR Data Spaces, contributing to next-generation data ecosystems. He leads the development of semantic tools and infrastructures, including contributions to Wikidata and CEUR-WS semantification. His lab work includes the Knowledge Graph Lab series, focusing on practical implementation and teaching of semantic technologies. Future work is expected to further explore the synergy between generative AI and formal knowledge representation to enhance data quality, reuse, and machine understanding.

Anisa RulaView profile

Assistant Professor

• Data Management
• Knowledge Graphs
• Data Quality Assessment
• +4 more

Anisa Rula is an Assistant Professor in Computer Science at the Department of Information Engineering, University of Brescia since 2021. Previously, she was a researcher at the University of Bonn's SDA group (2017–2019). She holds a PhD in Computer Science from the University of Milano-Bicocca (2014). Her research focuses on data management and exploration, particularly in data quality assessment for Information Systems and Knowledge Graphs. Key interests include semantic data enrichment, large datasets, and knowledge extraction. Education: PhD in Computer Science, University of Milano-Bicocca (2014) Prior academic and research roles at University of Bonn (2017–2019) Research Interests: Data Quality Assessment for Knowledge Graphs and Information Systems Large-Scale Dataset Management & Exploration Procedural Knowledge Extraction from Text Ontology-Driven Semantic Enrichment Human-in-the-Loop Data Annotation Systems Publications reflect her expertise in Knowledge Graph lifecycle management, quality assessment tools like KGHeartBeat, and LLM applications for schema mining and data discovery. Her work emphasizes practical implementations such as MantisTable for semantic table interpretation and K-hub ontology for industrial knowledge extraction. Awards: None explicitly mentioned in text. Advising & Grants: No student advisees listed. Active in developing open-source tools and collaborative frameworks for data quality and semantic web applications. Labs/Teams: Involved in Knowledge Graph validation and quality initiatives, including contributions to the EU Business Graph platform and cross-institutional workshops on Linked Data Quality.

• Knowledge Graphs
• Artificial Intelligence
• Machine Learning
• +11 more

Diego Reforgiato Recupero is an academic affiliated with the Università degli Studi di Cagliari, Italy. His work focuses on advancing artificial intelligence, knowledge graphs, and their applications in domains like healthcare, robotics, and digital transformation. He has collaborated extensively with researchers from institutions such as the University of Maryland, College Park (former affiliation). His research interests span Knowledge Graphs, Natural Language Processing, Machine Learning, and Robotics. Notable contributions include developing tools for knowledge graph construction (e.g., py-amr2fred) and applying AI techniques to solve real-world problems like job matching and virtual coaching systems. Diego’s publications (over 262 entries) reflect a strong emphasis on interdisciplinary applications of AI, including semantic web technologies, data science, and ensemble learning strategies. He has pioneered methods for integrating large language models with knowledge graphs to enhance scholarly research and industry applications. His work frequently addresses challenges in human-robot interaction, sentiment analysis for mental health, and ethical AI integration within cyber-physical systems. Current research trends indicate continued exploration of AI-driven solutions for education, finance, and healthcare sectors.

Raúl García-CastroView profile

Associate Professor

• Semantic Technologies
• Ontological Engineering
• Knowledge Graphs
• +3 more

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).

Konstantinos StefanidisView profile

Professor

• Data Science
• Big Data Management
• Recommender Systems
• +5 more

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.

Go SugimotoView profile

Research Fellow

• Linked Data
• Entity Linking
• Data Integration
• +4 more

Go Sugimoto holds multiple Visiting Fellow positions at Vrije Universiteit Amsterdam across key research units: Faculty of Science Business Web and Media Network Institute Intelligent Information Systems His research bridges Computer Science and Digital Humanities with emphases on: Quality assessment of Linked Data infrastructure Entity Linking for cultural heritage applications Data Integration challenges in historical research NLP-driven RDF generation from biographical texts FAIR principles implementation barriers Publication analysis reveals consistent focus on empirical evaluation of semantic web tools for humanities contexts, particularly examining data connectivity gaps in cultural heritage systems. His work demonstrates methodical investigation of real-world tool limitations while advancing practical integration frameworks for heterogeneous historical datasets. No major scientific awards are documented in available records. Information regarding student advising or research grant management is not disclosed in current profiles. Sugimoto operates within VU Amsterdam's Network Institute and Intelligent Information Systems ecosystem, leveraging interdisciplinary collaboration between computer science and humanities scholars to address data infrastructure challenges in cultural heritage domains.

Raymond WongView profile

Associate Professor

• Database Systems
• Data Science/Analytics
• Information Extraction
• +5 more

Associate Professor Raymond Wong is a faculty member in the School of Computer Science & Engineering at UNSW Sydney, where he also serves as Director of Online Education. He holds a PhD from Hong Kong University of Science & Technology, and has extensive expertise in database systems, data science, information extraction, and cloud computing. With over 220 conference papers, 56 journal articles, and two US patents, his research spans data mining, machine learning, and big data analytics. He has received 8 best paper awards and 4 industry/professional recognitions. His advisory roles include stints at Data61/CSIRO and Stats Central, and he has co-founded technology startups. Key projects include the Singapore NTU SPIRIT Smart Nation Research Centre's Intelligent Case Retrieval System (ICRS) and collaborations across Asia-Pacific regions. His research interests focus on database optimization, data analytics, NLP, and cloud infrastructure. He has supervised 25 PhD students and 65 honors students, six of whom earned University Medals. Notable grants include projects on ovarian cancer detection ($135k), cardiovascular disease analysis ($100k), and social media analytics ($250k). Awards include the 2016 ARC UNSW Supervisor Award and multiple IEEE best paper honors. His work bridges academia and industry, addressing challenges in healthcare, cybersecurity, and data-driven innovation.

• Dataspaces
• Semantic Web Technologies
• Ontologies
• +6 more

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.