STIndex: A Context-Aware Multi-Dimensional Spatiotemporal Information Extraction System

• URL: http://arxiv.org/abs/2604.08597v1
• Date: Tue, 07 Apr 2026 04:42:44 GMT
• Title: STIndex: A Context-Aware Multi-Dimensional Spatiotemporal Information Extraction System
• Abstract summary: textbfSTIndex is an end-to-end system that structures unstructured content into a multidimensional data warehouse.<n>textbfSTIndex integrates document-level memory, geocoding correction, and quality validation.<n>Live demonstration and open-source code are available at https://stindex.ai4wa.com/dashboard.
• Score: 25.729969757935436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Extracting structured knowledge from unstructured data still faces practical limitations: entity and event extraction pipelines remain brittle, knowledge graph construction requires costly ontology engineering, and cross-domain generalization is rarely production-ready. In contrast, space and time provide universal contextual anchors that naturally align heterogeneous information and benefit downstream tasks such as retrieval and reasoning. We introduce \textbf{STIndex}, an end-to-end system that structures unstructured content into a multidimensional spatiotemporal data warehouse. Users define domain-specific analysis dimensions with configurable hierarchies, while large language models perform context-aware extraction and grounding. \textbf{STIndex} integrates document-level memory, geocoding correction, and quality validation, and offers an interactive analytics dashboard for visualization, clustering, burst detection, and entity network analysis. In evaluation on a public health benchmark, \textbf{STIndex} improves spatiotemporal entity extraction F1 by 4.37\% (GPT-4o-mini) and 3.60\% (Qwen3-8B). A live demonstration and open-source code are available at https://stindex.ai4wa.com/dashboard.

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