Yuting Gao

Energy-domain tables often face significant challenges in semantic interoperability because semantically related concepts may be represented with inconsistent terminology, units, and structural formats across data sources. Large language models provide a promising solution due to their strong contextual reasoning ability, but their predictions can vary across models and simple aggregation strategies are often insufficient to ensure robust ontology-guided annotation. In this paper, we propose a multi-LLM framework for ontology-based semantic annotation of energy-domain tables. The framework combines ontology preprocessing, multi-model candidate generation, and a confidence-aware aggregation mechanism, termed Multi-LLM Soft Voting (MSV), to improve the reliability of semantic prediction. It supports both CTA-only setting and joint CTA+CPA setting, and further investigates how different ontology representations affect annotation quality and computational efficiency. We evaluate the proposed framework on real energy-domain tables using multiple commercial LLMs. The experimental results show that MSV consistently improves annotation performance over the baseline, while structured ontology input further reduces both computational time and LLM token cost.

Data anonymization is essential for data privacy, enabling organizations, especially in the energy sector with IoT data, to comply with regulations while using sensitive information. Despite many existing anonymization methods and tools, it lacks clarity on integrating these tools into a complete process and ensuring effective collaboration with data providers. To overcome these gaps, this paper proposes a collaborative data anonymization framework that efficiently chains open-source tools to streamline the process and improve data provider involvement. The findings indicates while these tools can detect sensitive information and generate anonymizeddata, there are still limitations in metadata detection.