Open Research Knowledge Graph (ORKG) API

Overview

The Open Research Knowledge Graph (ORKG) transforms unstructured scholarly articles into structured, machine-readable research contributions. Unlike traditional databases that store metadata (title, authors, DOI), ORKG captures the semantic content — research problems, methods, results, and their relationships. The REST API enables querying, creating, and comparing research contributions programmatically. Free, no authentication required for read operations.

API Endpoints

Base URL

https://orkg.org/api/

Search Papers

# Search papers in ORKG
curl "https://orkg.org/api/papers?q=climate+change+adaptation&size=20"

# Get paper details by ID
curl "https://orkg.org/api/papers/R12345"

Search Resources

# Search any resource (papers, predicates, comparisons)
curl "https://orkg.org/api/resources?q=machine+learning&size=20"

# Filter by class
curl "https://orkg.org/api/resources?q=BERT&exact=false&classes=Paper"

Comparisons

ORKG's unique feature — structured side-by-side comparison of papers:

# List comparisons
curl "https://orkg.org/api/comparisons?size=10"

# Get a specific comparison
curl "https://orkg.org/api/comparisons/R54321"

# Search comparisons
curl "https://orkg.org/api/comparisons?q=sentiment+analysis"

Research Contributions

# Get contributions of a paper
curl "https://orkg.org/api/papers/R12345/contributions"

# A contribution describes what a paper contributes:
# - Research problem addressed
# - Method used
# - Results achieved
# - Materials/datasets used

Python Usage

import requests

BASE_URL = "https://orkg.org/api"

def search_orkg_papers(query: str, size: int = 20) -> list:
    """Search papers in the Open Research Knowledge Graph."""
    resp = requests.get(f"{BASE_URL}/papers", params={"q": query, "size": size})
    resp.raise_for_status()
    data = resp.json()

    papers = []
    for item in data.get("content", []):
        papers.append({
            "id": item.get("id"),
            "title": item.get("title"),
            "created": item.get("created_at"),
            "contributions": item.get("contributions", [])
        })
    return papers

def get_paper_contributions(paper_id: str) -> dict:
    """Get structured research contributions for a paper."""
    resp = requests.get(f"{BASE_URL}/papers/{paper_id}/contributions")
    resp.raise_for_status()
    return resp.json()

def search_comparisons(topic: str) -> list:
    """Find structured paper comparisons on a topic."""
    resp = requests.get(f"{BASE_URL}/comparisons", params={"q": topic, "size": 10})
    resp.raise_for_status()
    return resp.json().get("content", [])

# Example usage
papers = search_orkg_papers("transfer learning NLP")
for p in papers:
    print(f"[{p['id']}] {p['title']}")

comparisons = search_comparisons("named entity recognition")
for c in comparisons:
    print(f"Comparison: {c.get('title')} ({len(c.get('contributions', []))} papers)")

Key Concepts

Concept	Description	Example
Paper	A scholarly article with metadata	"Attention Is All You Need"
Contribution	What a paper contributes to knowledge	"Proposes self-attention mechanism"
Research Problem	The problem a contribution addresses	"Machine translation quality"
Predicate	A relationship type	"has_method", "has_result", "uses_dataset"
Comparison	Side-by-side structured comparison	"Transformer variants comparison"
Resource	Any entity in the knowledge graph	A method, dataset, metric, or concept

ORKG vs Traditional Databases

Feature	Traditional (S2, Crossref)	ORKG
Content	Metadata (title, DOI, citations)	Semantic content (methods, results)
Structure	Flat records	Knowledge graph with relationships
Comparison	Manual (read each paper)	Automated structured comparisons
Machine-readable	Bibliographic metadata only	Research contributions structured
Coverage	Broad (200M+ papers)	Deep but narrower (~50K papers)

Use Cases

1. Literature surveys: Find existing comparisons to quickly understand a field
2. Method selection: Compare methods across papers on structured criteria
3. Gap analysis: Identify research problems without solutions
4. Reproducibility: Access structured descriptions of experimental setups

References

• ORKG Website
• ORKG API Documentation
• ORKG Help Center
• Jaradeh, M.Y., et al. (2019). "Open Research Knowledge Graph: Next Generation Infrastructure for Semantic Scholarly Knowledge." K-CAP 2019.