Traversing Citation Networks

Response:

{
  "paperId": "abc123def456",
  "title": "Paper Title",
  "year": 2023
}

Save paperId - needed for citations/references queries

2. Backward Traversal (References)

Get references from paper:

curl "https://api.semanticscholar.org/graph/v1/paper/abc123def456/references?fields=contexts,intents,title,year,abstract,externalIds&limit=100"

Response format:

{
  "data": [
    {
      "citedPaper": {
        "paperId": "xyz789",
        "title": "Referenced Paper Title",
        "year": 2020,
        "abstract": "...",
        "externalIds": {
          "DOI": "10.5678/referenced.2020",
          "PubMed": "87654321"
        }
      },
      "contexts": [
        "...as described in previous work [15]...",
        "...we used the method from [15] to..."
      ],
      "intents": ["methodology", "background"]
    }
  ]
}

Filter for relevance:

For each reference, check:

1. Context keywords: Do citation contexts mention user's query terms?
   • Example: If user asks about "IC50 values", look for contexts mentioning "IC50", "activity", "potency"
2. Title match: Does title contain relevant keywords?
3. Intent: Is intent "methodology" or "result" (more relevant) vs "background" (less relevant)?

Scoring:

• Context keywords match: +3 points
• Title keywords match: +2 points
• Intent is methodology/result: +2 points
• Recent (< 5 years old): +1 point

Only add to queue if score ≥ 5

3. Forward Traversal (Citations)

Get papers citing this one:

curl "https://api.semanticscholar.org/graph/v1/paper/abc123def456/citations?fields=title,year,abstract,externalIds&limit=100"

Response format:

{
  "data": [
    {
      "citingPaper": {
        "paperId": "def456ghi",
        "title": "Newer Paper Citing This",
        "year": 2024,
        "abstract": "We extended the work of [original paper]...",
        "externalIds": {
          "DOI": "10.9012/citing.2024"
        }
      }
    }
  ]
}

Filter for relevance:

For each citing paper:

1. Title match: Keywords present in title?
2. Abstract match: User's query terms in abstract?
3. Recency: Newer papers often build on findings (prioritize < 2 years)
4. Citation count: If Semantic Scholar provides, highly cited papers more likely relevant

Scoring:

• Title keywords match: +3 points
• Abstract keywords match: +2 points
• Recent (< 2 years): +2 points
• Moderate recency (2-5 years): +1 point

Only add to queue if score ≥ 5

4. Deduplication

Before adding to queue:

Check papers-reviewed.json:

doi = paper["externalIds"].get("DOI")
if doi in papers_reviewed:
    skip  # Already processed