Scenario pain points
Traditional contract management systems rely on keyword searches, making it difficult to understand semantic relationships such as "liability for breach of contract" and "payment terms".
Implementation steps
- Building a contract ontology: generating an initial ontology using a sample contract
uv run ontocast build-ontology --input contracts/ - Batch processing of documents: the contract PDF of the previous year into the data/contracts/ directory to perform batch processing
- Building GraphRAG: Configuration
ENABLE_GRAPH_RAG=trueEnabling semantic search
Typical Query Example
- SPARQL queries for terms greater than $1 million:
SELECT ?clause WHERE { ?clause :hasAmount ?amt. FILTER(?amt > 1000000) } - Find associated entities:
查询"甲方违约"关联的所有责任条款
Effectiveness evaluation
Compared to traditional search:
- Accuracy improvement 40-60%
- Reduced response time for correlation queries from minutes to seconds
- Automatic discovery of hidden clause associations (e.g., cross-references)
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 apply OntoCast in enterprise contract management scenarios to improve retrieval efficiency?
