Call for papers: Special Issue on

Geospatial Knowledge Graphs

Knowledge graphs (KGs) are rooted in Semantic Web technologies, an extension of the World Wide Web that allows data to be published in a model that is both human-readable and machine-interpretable. Since space and place often serve as key nexuses in knowledge graphs—linking diverse entities such as people, organizations, events, and more—Geospatial Knowledge Graphs (GeoKGs) have emerged. GeoKGs emphasize the role of spatial and temporal dimensions within the KGs, supporting geospatial data organization, retrieval, and synthesis. Notable GeoKGs include YAGO2, LinkedGeoData, GeoNames, GNIS-LD, KnowWhereGraph, among others. With the advancement of AI and Geospatial AI technologies, GeoKGs have become a promising toolset for organizing and synthesizing geospatial data into structured formats, enabling various applications like Geospatial Data Integration, Geoparsing, Geographic Information Retrieval, and Geographic Question Answering.

In recent years, the rise of large foundation models (LFMs)—such as GPT-4, LLAVA, Gemini—and retrieval-augmented generation (RAG) technologies has underscored the growing importance of knowledge graphs in cutting-edge AI applications, especially following the release of Microsoft's GraphRAG package. This raises several intriguing research questions: How can we develop KGs and GeoKGs that are better suited for current LFM and RAG-based applications? How can GeoKGs, particularly their geospatial information, be leveraged to inform LFMs for fairer, more accurate, and up-to-date predictions? How the LFMs can be used to enhance the development of GeoKGs?

This special issue aims to address these questions, among others, by seeking new methods, models, and resources that advance research in Geospatial Knowledge Graphs and GeoAI. Relevant topics include, but are not limited to:

• Geospatial Knowledge Graph Construction
• Geo-Ontology Engineering
• Geographic Ontology Alignment
• Geographic Entity Similarity Measurement (Alignment)
• Automatically GeoKG Construction from Unstructured Text
• Coreference Resolution for Geographic Entities
• Spatio-Temporal Scoping of Knowledge Graphs
• Geospatial Knowledge Graph Management
• Geospatial Knowledge Graph Completion
• GeoSPARQL and Spatial Query Evaluation
• Federated Query across Geospatial Knowledge Graphs
• GeoKG-based Geovisualization and Decision Support
• Virtual GeoKGs
• Query Geospatial Knowledge Graphs
• GeoSPARQL and spatial query evaluation
• Indexing geospatial knowledge graphs
• Spatial query benchmark
• Downstream Applications on Geospatial Knowledge Graphs
• Geographic Question Answering and Semantic Parsing based on Knowledge Graphs
• Geospatial Knowledge Graph Summarization
• Geospatial Data Integration
• Geographic Information Retrieval
• Geospatial Visualization
• Geo-Text Analysis
• Geospatial Information Extraction
• Toponym Recognition and Toponym Resolution
• Geospatial-Aware Language Model Development
• Deep Learning on Geospatial Knowledge Graphs
• Geographic Knowledge Graph Embeddings
• Spatiotemporally explicit machine learning on KGs and GeoKGs
• GraphRAG on GeoKGs
• GeoKG development for/with LFM and RAG

Submission Guideline

Submission deadline: March 03, 2025. Papers submitted before the deadline will be reviewed upon receipt.

Author Guidelines

Submissions should be made through the Semantic Web Journal website at http://www.semantic-web-journal.net.
Authors must take notice of the submission guidelines posted at http://www.semantic-web-journal.net/authors.

Guest Editors:

• Prof. Gengchen Mai, Department of Geography and the Environment, University of Texas at Austin
• Dr. Weiming Huang, School of Geography, University of Leeds
• Dr. Jinmeng Rao, Google DeepMind
• Prof. Rui Zhu, School of Geographical Sciences, University of Bristol
• Ms. Meilin Shi, Department of Geography and Regional Research, University of Vienna