Research knowledge graph construction can be divided into four key steps:
- Data preparation phase::
utilizationingest_directoryBatch import thesis PDF, recommended settings:rules=[{"type":"metadata_extraction","schema":{"doi":"string","keywords":"list"}}] - Physical extraction::
The system recognizes it automatically:
- Research subjects (e.g., "convolutional neural networks")
- Research methods (e.g., "comparative experiments")
- Academic relationships (e.g., "citation", "improvement") - Atlas generation::
fulfillmentcreate_graphCommand:db.create_graph("ai_research", filters={"domain":"computer_vision"}, relation_depth=3) - Intelligent Search::
pass (a bill or inspection etc)hop_depthParameters control association breadth:db.query("CNN在医疗影像中的应用", graph_name="ai_research", hop_depth=2)
advanced skill::
- Combined with ColPali to retrieve experimental diagrams from papers
- set upcache_documentsAccelerated high-frequency access to documentation
- Automatic labeling of unlabeled datasets using natural language rules
The program has been adopted by several research organizations and has reduced the average literature research time by 70%.
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 to build a research knowledge graph with Morphik Core?
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