Visualization Innovations for Transparent Research Processes
The key to differentiate KResearch from conventional AI tools is the dual safeguard mechanism of its real-time research log system and visualized knowledge graph. The real-time log in the right panel accurately records each research step, including 12 types of operation records, such as search keyword generation, web page priority evaluation, content extraction strategy, etc. This granular transparency allows users to completely trace the AI decision chain. In the actual test, when dealing with complex topics such as "blockchain cross-chain technology", the system generates an average of 83 operation logs, with each step labeled with the time consumed and the result status.
The knowledge graph function has made a qualitative breakthrough by extracting key entities (people, organizations, technical terms, etc.) in the research topic through the Named Entity Recognition (NER) technology, and then using the graph database principle to build an association model. For example, in the study of "gene editing ethics", the system automatically identifies the core nodes such as CRISPR and Zhang Feng, and builds up a three-dimensional relationship network of "technology, law and ethics". This visualization makes it possible to improve the efficiency of understanding complex information by 40%, which is especially helpful for the literature review of academic papers.
This answer comes from the articleKResearch: Generate Free In-depth Research Reports with Google Gemini APIsThe
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