arXiv cs.AI· Lingxiao Kong, Vasileios Arampatzakis, Vasileios Sevetlidis, George Pavlidis, Zeyd Boukhers·· 3 小时前AI 评分35
HeriKGQA：面向文化遗产传感器数据的知识图谱问答方法
HeriKGQA: Grounded Knowledge-Graph Question Answering over Cultural Heritage Sensor Data
AI 导读
HeriKGQA 是一种知识图谱问答方法，让 LLM 仅负责理解问题和组织答案，计算交由 SPARQL 和确定性代码完成。在五个文化遗产地的 500 个真实数据问题上，其准确率达 99.0%，比使用相同 LLM 的最强基线高 18.2 个百分点，速度接近四倍；去掉 grounding 后准确率下降 12.2 个百分点，异常类问题从 99% 降至 66%。
正文 · 原文
Abstract:Cultural heritage sites are increasingly monitored by sensor networks, but querying their data requires database expertise. Large Language Models (LLMs) offer a natural-language interface, yet over raw sensor records they lose track of which sensor, site, and time each value belongs to, and silently miscompute. We present HeriKGQA, a knowledge-graph question-answering method that confines the LLM to interpreting questions and phrasing answers, leaving computation to SPARQL and deterministic code. Questions are parsed into intents constrained by a heritage ontology aligning W3C SOSA/SSN with CIDOC-CRM, compiled into SPARQL, and answered from query results and temporal, spatial, and anomaly facts derived by a grounding layer, with error correction guarding each LLM step. On 500 questions over real data from five heritage sites, HeriKGQA reaches 99.0% accuracy, 18.2 points above the strongest of three baselines using the same LLM and receiving the same anomaly rules and answer conventions, with comparable answer quality while running almost four times faster. Without grounding, accuracy drops by 12.2 points and on anomaly questions from 99% to 66%. We report lessons from development and live deployment.
| Comments: | Submitted to NAACL 2027 industry track |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2611.15904 [cs.CL] |
| (or arXiv:2611.15904v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15904 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lingxiao Kong [view email]
[v1]
Wed, 11 Nov 2026 19:09:13 UTC (14,656 KB)
来源：arXiv cs.AI · arxiv.org