Narrow and Deep: An Ontology Tower as the Knowledge of an LLM Agent for an Industrial Equipment System
This study proposes the ontology tower, a narrow-and-deep ontology of a single equipment system whose knowledge deepens in two ways: through quantities derived from the measured points by physical relations, and through lessons from the operating journal incorporated as knowledge nodes.
ProofPaper ↗
Key points
- Large language model (LLM) agents are beginning to operate industrial energy equipment, and what they get right depends on what they are told about the plant.
- Established building ontologies name many kinds of points across many sites, whereas an industrial equipment system needs few entities with much knowledge about each.
- On a real low-humidity air-handling test plant operated daily through a programmable logic controller, agents received a text projected from its tower in a preregistered evaluation of nine tasks replayed from the plant's records, using four open-weight models from 9 to about 750 billion parameters.
- An ontology narrow in entities but deep in what is known about them can thus supply the knowledge that an agent for an industrial equipment system needs.
Sources (1)
- [1]Narrow and Deep: An Ontology Tower as the Knowledge of an LLM Agent for an Industrial Equipment SystemarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 11:50 AM
This study proposes the ontology tower, a narrow-and-deep ontology of a single equipment system whose knowledge deepens in two ways: through quantities derived from the measured points by physical relations, and through lessons from the operating journal incorporated as knowledge nodes.
Large language model (LLM) agents are beginning to operate industrial energy equipment, and what they get right depends on what they are told about the plant.
Extractive summary: sentences quoted from the sources.