A four-step approach to building a domain knowledge graph
To address the issue of "information silos" in scientific research literature, the following process can be followed:
- Data preparation: Use of
ingest_directory('papers/')Batch import PDF documents, it is recommended to addmetadata={'domain':'biomedical'}and other discipline labels. - map construction: Implementation
create_graph()time-critical configurationentity_types=["基因","疾病"]Define extraction goalsrelationship_types=["调控","治疗"]Declaration of affiliation
- Intelligent Search: By
query("PTEN基因相关的癌症治疗方法", hop_depth=2)Realization:- Literature on direct association of first tier matched PTEN genes
- Extended search of the literature on treatments at the second level
- Continuous optimization: Monthly for
update_graph()Incremental updating of the mapping withprune_edges(min_weight=0.3)Prune weak associations.
The efficiency of cross-literature correlation discovery was improved by 6 times after application in an oncology institute.
This answer comes from the articleMorphik Core: an open source RAG platform for processing multimodal dataThe
May not be reproduced without permission:AI productivity tools " How can knowledge graphs be applied to enhance association discovery in research literature management?
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