Performance Bottleneck Analysis
Academic papers usually contain a lot of jargon and complex sentences that can easily lead to:
- memory overflow
- Processing timeout
- Elevated error rates for physical links
Tuning program
- Hardware level::
- set upDOCKER_MEM_LIMIT=8g
- Allocate separate SSD storage for Fuseki - parameter optimization::
–ESTIMATED_CHUNKS=论文页数*3
–MAX_TOKENS_PER_CHUNK=2048 - Process Optimization::
- Staged processing: extracting metadata before parsing the text
- enable--incrementalincremental processing mode
Domain Adaptation Tips
- Preloaded subject ontologies (e.g., MeSH Medical Thesaurus)
- configure
ACADEMIC_MODE=trueEnable special handling of formulas/quotations - utilization
--skip-referencesSkip reference parsing
Monitoring Recommendations
pass (a bill or inspection etc)docker statsMonitor memory usage when exceeding 70%:
1. IncreaseRECURSION_LIMIT
2. DowngradingLLM_TEMPERATUREReduced generation of variants
This answer comes from the articleOntoCast: an intelligent framework for extracting semantic triples from documentsThe
May not be reproduced without permission:AI productivity tools " How to optimize OntoCast's performance issues when processing long academic papers?
