arXiv:2607.08177v1 Announce Type: new Abstract: In this paper, we study the automatic schema generation problem: given a collection of historical ship maintenance and operational reports across multiple form categories, automatically discover compact and informative schemas that capture the essential information requirements of each report type. To address this challenge, we propose ASMR, a modular agentic framework consisting of two specialized agents. A Field Generation Agent extracts semantic concepts from historical narratives and generates candidate schema fields through adaptive multi-gr
The proliferation of language models enables new approaches to automating complex analytical tasks, making agentic systems for data extraction and schema generation increasingly feasible.
Automated schema generation for maintenance reports can significantly reduce manual effort, improve data quality, and accelerate insights, particularly in sectors with extensive operational reporting.
The development proposes a modular framework that allows for more sophisticated and adaptive automation of unstructured data analysis compared to traditional rule-based systems.
- · Shipping industry
- · Maritime logistics
- · AI software providers
- · Industrial maintenance
- · Manual data entry services
- · Legacy data analysis tools
Increased efficiency and accuracy in ship maintenance reporting and analytics are the immediate benefits.
Improved predictive maintenance capabilities and reduced operational downtime across maritime operations could follow.
The methodology could generalize to other industries requiring automated extraction and structuring of complex operational reports, accelerating adoption of agentic AI in enterprise workflows.
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Read at arXiv cs.AI