Evidence Graph

An evidence graph is a structured representation of reasoning around a claim:

• An evidence graph is a directed graph where nodes represent claims, evidence, justifications, rebuttals, or counter-arguments, and edges represent the relationships between them (e.g., “supports,” “rebuts,” “justifies”).
• The purpose is to make explicit how a central claim is supported or challenged, showing chains of reasoning and the strength or weakness of arguments.

Where evidence graphs are used

• Scientific Research & Academia 🧪📚
• To map how studies, datasets, and methods link together.
• Helps researchers trace the origins and reliability of findings.
• Healthcare & Clinical Trials 🏥💉
  • Used to link clinical trial results, patient outcomes, and supporting literature.
  • Ensures medical guidelines are based on transparent chains of evidence.
• Policy & Decision-Making 🏛️⚖️
• Governments and NGOs use them to justify policies with traceable, data-backed reasoning.
• Supports accountability and transparency in public decisions.
• Data Science & AI 🤖📊
  • Provides provenance (where data comes from) and helps explain AI/ML model decisions.
  • Makes automated systems more trustworthy.
• Legal & Compliance 📜🔍
  • Helps build chains of verified evidence in court cases or audits.
  • Ensures that claims are backed with documented proof.

Benefits of evidence graphs

• Transparency 🔎
  • Every claim or conclusion can be traced back to its supporting evidence.
• Trust & Credibility 🤝
  • Decision-makers, researchers, and the public gain confidence in results.
• Reproducibility 🔄
  • By showing how evidence was collected and connected, others can replicate findings.
• Efficiency ⚡
  • Saves time by clearly mapping relationships instead of sifting through raw data.
• Integration of Multiple Sources 🌐
  • Allows combining diverse data (papers, datasets, expert input) into a single structured view.