In the last two decades, knowledge graphs (KGs) have seen increased interest thanks to their suitability for representing relations (edges) between entities (nodes), and their semi-structured nature, which eases the integration of diverse, heterogeneous data sources. This tutorial will introduce its participants to the latest advancements in the area of geospatial knowledge graphs (GeoKGs) and how they are used as core technologies in projects like FAIR2ADAPT to unlock new insights and facilitate the creation of modern, intelligent applications.
GeoKGs are knowledge graphs that contain geospatial knowledge. This knowledge is encoded using either longitude/latitude pairs or detailed geometries (e.g., lines, polygons, multipolygons etc.) which are more appropriate for modeling the geometries of features such as rivers, roads, countries etc. GeoKGs are used in a variety of GIS appications like geospatial question-answering, disaster response, earth observation, flood assesment, and electoral analysis among others.
GeoKG-2025 is a full-day tutorial, as seen in following schedule:
Time | Session |
---|---|
09:30 - 11:00 |
Introduction to Knowledge Graphs The basic concepts of KGs and GeoKGs. Sources of geospatial information. The knowledge graphs DBpedia, Wikidata, YAGO2, YAGO2geo, WorldKG, KnowWhereGraph, H3-GeoKG, CrowdGeoKG, and HGeoKG. |
10:30 - 11:00 | Coffee Break |
11:00 - 12:30 |
Methodologies for Geospatial Knowledge Graph Construction Geospatial data integration and interlinking. The tool JedAI-spatial. |
12:30 - 14:00 | Lunch Break |
14:00 - 15:30 |
Question Answering over Geospatial Knowledge Graphs The engines GeoQA2, Questions-To-GeoSPARQL, and TerraQ. The evaluation dataset GeoQuestions1089. How LLMs can advance the field of geospatial question-answering. How question answering systems enhance the accessibility of data and the real world example of FAIR2ADAPT. |
15:30 - 16:00 | Coffee Break |
16:00 - 17:30 | Applications of Geospatial Knowledge Graphs |
17:30 - 18:00 | Discussion |
Sergios-Anestis Kefalidis is a Ph.D. student and Research Assistant in the AI Team of the National and Kapodistrian University of Athens. He holds a B.Sc. and a MSc. in Computer Science from the National and Kapodistrian University of Athens. He has actively participated in multiple European research projects (DA4DTE, GeoQA, STELAR). His research focuses on universal question-answering over knowledge graphs and applications for geospatial question-answering systems. His Ph.D. research is supported by the “NIK. D. XRYSOVERGI” scholarship of the Greek State Scholarship Foundation. Before his involvement with the AI Team, during his undergraduate studies, he participated in Google Summer of Code 2020, 2021 and 2022. During this time he was an active contributor of open-source software and has even served as a core developer and maintainer for the widely used Xfce desktop-environment for UNIX-like systems.
Kostas Plas is a Ph.D. student and Research Assistant in the AI Team of the National and Kapodistrian University of Athens. He holds a BSc. and a MSc. in Computer Science from the National and Kapodistrian University of Athens. His research focuses on optimizing graph databases and geospatial triple stores as well as spatial reasoning in LLMs. His Ph.D. is supported by the scholarship with code “GD.402. ARCHI-YPPhD-0824” from the Archimedes Research Unit. He has gathered experience in geospatial knowledge graph and knowledge graph augmentation and manipulation through the usage of LLMs in his participation on the European projects DA4DTE and STELAR.
Manolis Koubarakis is a Professor and Director of Graduate Studies in the Dept. of Informatics and Telecommunications of the National and Kapodistrian University of Athens, where he directs the AI Team. He is also an affiliated researcher of the Archimedes Unit, a Fellow of EurAI since 2015 and Member of the EurAI Board since 2022. He has published more than 250 papers that have been widely cited (8973 citations and h-index 52 in Google Scholar) in the areas of Artificial Intelligence (especially Knowledge Representation and Constraint Programming), Databases (especially temporal and spatial databases), Semantic Web, Knowledge Graphs and Linked Data (especially geospatial and Earth observation data), and Deep Learning for Natural Language Processing. Manolis has taught at university level since 1995 and is a recognized AI researcher in spatial reasoning, geospatial databases, and knowledge graphs. He has given many tutorials and has led many laboratories for undegraduate and graduate courses.
Cogan Shimizu is an Assistant Professor at Wright State University, USA, in the Department of Computer Science and Engineering, where he directs the Knowledge and Semantic Technologies (KASTLE) Lab. His work broadly spans the field of knowledge engineering, but it is rooted in pattern-based design and implementation of modular ontology. These techniques have been applied across many domains, including digital history, geospatial knowledge graphs, logistics, and education. By now, he has also pivoted into foundational research for neurosymbolic artificial intelligence across these domains. Cogan has authored over 100 peer reviewed articles in highly regarded venues. Cogan has been teaching undergraduate and graduate courses since 2015, and has helped deliver many tutorials at ISWC, ESWC, and WWW.
KnowWhereGraph was developed in the context of "Convergence Accelerated Program Grant OIA - 2033521"
"NIK. D. XRYSOVERGI" PhD scholarship
"GD.402. ARCHI-YPPhD-0824" PhD scholarship