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GeoOutageBench: Benchmarking Ambiguity-aware, Ontology-grounded Geospatiotemporal KGQA for Multimodal Power Outage and Resilience Analysis — arXiv:2609.36082

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.36082
License (as archived): CC0-1.0
Archived: 2026-10-07T16:22:15.512Z
SHA-256 of the archived text: ff3bd3314f550877aa65a7d4a7cafc105050caac482f774d814d3237b8b6eb26
Stable URL of this page: https://metraipolis.com/documento/dc_ff855702ddff

How to cite

GeoOutageBench: Benchmarking Ambiguity-aware, Ontology-grounded Geospatiotemporal KGQA for Multimodal Power Outage and Resilience Analysis — arXiv:2609.36082 — arXiv (en), https://arxiv.org/abs/2609.36082 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff855702ddff (Metraipolis archive dc_ff855702ddff, sha256 ff3bd3314f550877aa65a7d4a7cafc105050caac482f774d814d3237b8b6eb26)

BibTeX

@misc{dcff855702ddff,
  title = {GeoOutageBench: Benchmarking Ambiguity-aware, Ontology-grounded Geospatiotemporal KGQA for Multimodal Power Outage and Resilience Analysis — arXiv:2609.36082},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.36082},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff855702ddff; sha256 ff3bd3314f550877aa65a7d4a7cafc105050caac482f774d814d3237b8b6eb26},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.36082. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We introduce GeoOutageBench, a benchmark for assessing LLM-based geospatiotemporal KGQA for multimodal outage and resilience analysis. Unlike existing KGQA benchmarks for Web knowledge, GeoOutageBench considers a spatiotemporal KG that integrates visual, textual, and structured data from outage records, remote sensing, weather observations, storm and power events, geographic entities, and domain ontologies. It provides a competency query taxonomy at different difficulty levels from spatiotemporal containment and proximity, spatiotemporal co-occurrence analysis, multimodal evidence, to hypothetical evaluation. Over multimodal KG and query classes, GeoOutageBench provides user-configurable evaluation of three important, highly coherent yet less studied tasks: (1) LLMs' understanding for ambiguous geospatiotemporal questions in terms of NL to SPARQL interpretation, (2) query-driven assessment of ontology utility, and (3) answer accuracy of multimodal KGQA retrieval. GeoOutageBench provides a design principle and foundation for assessing LLM-KG systems that support real-world infrastructure resilience analysis. Our benchmark, source code, data, results, and other documentation are available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-30 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Information Retrieval (cs.IR).
Pagina del lavoro: https://arxiv.org/abs/2609.36082 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T15:54:53.465Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).