paper-decomposition

To extract research questions and methodology chains from academic papers, use paper decomposition to parse the text and build a structured knowledge graph. It identifies hypotheses, problem statements, and maps the complete methodology pipeline automatically.

Yes, you can build a directed claim dependency graph to map empirical claims to their supporting evidence. This evidence graph visually structures the logical relationships and dependencies found within the academic paper.

The best way to generate a structured knowledge graph from a research paper is using paper decomposition, which outputs a JSON knowledge graph, a visual map, and a Markdown summary detailing the internal logical structure and claims hierarchy.

Yes, you need Python and various libraries for text parsing and graph construction to extract claims and build a JSON knowledge graph. The process relies on specific scripts to parse rhetorical structures and construct the directed graphs.

Cross-paper linking works by connecting multiple academic papers in the same domain within the generated knowledge graph. This allows users to map shared methodology pipelines and related empirical claims across different research texts.

Limitations of paper decomposition for research analysis include its dependency on Python and external libraries for text parsing, and the prerequisite that input papers must contain clearly defined rhetorical structures to accurately build the methodology pipeline and evidence graph.