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The integration of quantum computing with knowledge graphs presents a transformative approach to intelligent information processing that enables enhanced reasoning, semantic understanding, and large-scale data inference. This study introduces a Quantum Knowledge Graph (QKG) framework that combines Neo4j’s LLM Knowledge Graph Builder with Quantum Natural Language Processing (QNLP) to improve the representation, retrieval, and inference of complex knowledge structures. The proposed methodology involves extracting structured relationships from unstructured text, converting them into quantum-compatible representations using Lambeq, and executing quantum circuits via Qiskit to compute quantum embeddings. Using superposition and entanglement, the QKG framework enables parallel relationship processing, contextual entity disambiguation, and more efficient semantic association. These enhancements address the limitations of classical knowledge graphs, such as deterministic representations, scalability constraints, and inefficiencies in the capture of complex relationships. This research highlights the importance of integrating quantum computing with knowledge graphs, offering a scalable, adaptive, and semantically enriched approach to intelligent data processing.
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This content will become publicly available on January 1, 2026
Precomputed Topological Relations for Integrated Geospatial Analysis Across Knowledge Graphs
Geospatial Knowledge Graphs (GeoKGs) represent a significant advancement in the integration of AI-driven geographic information, facilitating interoperable and semantically rich geospatial analytics across various domains. This paper explores the use of topologically enriched GeoKGs, built on an explicit representation of S2 Geometry alongside precomputed topological relations, for constructing efficient geospatial analysis workflows within and across knowledge graphs (KGs). \r\nUsing the SAWGraph knowledge graph as a case study focused on enviromental contamination by PFAS, we demonstrate how this framework supports fundamental GIS operations - such as spatial filtering, proximity analysis, overlay operations and network analysis - in a GeoKG setting while allowing for the easy linking of these operations with one another and with semantic filters. This enables the efficient execution of complex geospatial analyses as semantically-explicit queries and enhances the usability of geospatial data across graphs. Additionally, the framework eliminates the need for explicit support for GeoSPARQL’s topological operations in the utilized graph databases and better integrates spatial knowledge into the overall semantic inference process supported by RDFS and OWL ontologies.
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- Award ID(s):
- 2333782
- PAR ID:
- 10640764
- Editor(s):
- Sila-Nowicka, Katarzyna; Moore, Antoni; O'Sullivan, David; Adams, Benjamin; Gahegan, Mark
- Publisher / Repository:
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Date Published:
- Edition / Version:
- 1
- Volume:
- 346
- ISSN:
- 1868-8969
- Page Range / eLocation ID:
- 4:1-4:22
- Subject(s) / Keyword(s):
- knowledge graph GeoKG spatial analysis ontology SPARQL GeoSPARQL discrete global grid system S2 geometry GeoAI PFAS Computing methodologies → Spatial and physical reasoning Computing methodologies → Ontology engineering
- Format(s):
- Medium: X Size: 22 pages; 2995776 bytes Other: application/pdf
- Size(s):
- 22 pages 2995776 bytes
- Location:
- 13th International Conference on Geographic Information Science (GIScience 2025), Christchurch, New Zealand, Aug. 2025
- Sponsoring Org:
- National Science Foundation
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