Background to the issue
Interdisciplinary research requires the integration of literature resources from multiple fields, and traditional methods suffer from pain points such as one-sided access to information and difficulties in understanding terminology.
Multi-Intelligence Solutions
- Specialized division of labor: specialized analytical intelligences for each discipline
- Knowledge Graph Construction: Automatically Building Cross-Domain Conceptual Association Networks
- Terminology conversion: mapping conversion of terminology from different disciplines
Specific realization steps
- Configuring the multidisciplinary analysis module: modifying the config/agents.yaml file
- Enter key literature or search terms for each discipline
- Run python main.py -task "cross_disciplinary_analysis"
- View the generated comprehensive analysis report and knowledge correlation map
Best Practice Recommendations
It is recommended to conduct small-scale tests to assess the effectiveness of collaboration of different intelligences before scaling up to a full study. Priority can be given to subject areas that are supported by open literature databases.
This answer comes from the articleTHESIS Agent: An Intelligent Tool to Assist in Writing Academic PapersThe
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