n4o-graph-api

API and minimal web interface to the NFDI4Objects Knowledge Graph (N4O KG)

This repository implements public web APIs to the NFDI4Objects Knowledge Graph, available at https://graph.nfdi4objects.net/. The Knowledge Graph internally consists of an RDF Triple Store and a Labeled Property Graph. These databases can be queried with SPARQL and with Cypher respectively using the API endpoints provided by this web application. In addition, collection URIs starting with https://graph.nfdi4objects.net/collection/ are served as linked open data from the triple store.

For background information see the Knowledge Graph Manual (in German).

Table of Contents

• Installation
• Configuration
• Usage
  • SPARQL API
  • Property Graph API
  • Linked Open Data
• Development
• License

Installation

Requires Python >= 3.5. Python modules are listed in requirements.txt. Use deployment method of your choice. The application must be configured first.

Configuration

A local file config.yaml is needed with configuration. Use this as boilerplate:

cypher: 
  uri: "bolt://localhost:7687"
  user: ""
  password: "" 
  timeout: 30
  examples:
    - name: Get some people
      query: "MATCH (n:E21_Person) RETURN n LIMIT 10"
    - name: List all classes (= node labels)
      query: "MATCH (n)\n RETURN distinct labels(n) AS classes, count(*) AS count"
sparql:
  endpoint: "https://dbpedia.org/sparql"
  examples:
    - queries/*.rq
    - name: List all classes
      query: |
        SELECT DISTINCT ?class WHERE { [] a ?class }  examples:

Make sure the Neo4j (or compatible) database is read-only because this application only applies a simple filter to detect Cypher write queries!

Usage

SPARQL API

This webservice implements SPARQL query API at /api/sparl. The query is transformed to a POST request and passed to the backend SPARQL endpoint.

Property Graph API

The Property Graph API at /api/cypher expects a HTTP GET query parameter query with a Cypher query or a HTTP POST request with a Cypher query as request body. The return format is a (possibly empty) JSON array of result objects. On failure, an error object is returned. Each response objects is maps query variables to values. Each value is one of:

• number, string, boolean, or null
• array of values
• PG-JSONL node or edge object for nodes and edges
• PG-JSON graph object for pathes

The following examples use n4o-graph-apis application running at https://graph.nfdi4objects.net/ for illustration. This URL will be changed! Use base URL http://localhost:8000/ for testing a local installation.

Query with Python

import requests
import json

api = "https://graph.nfdi4objects.net/api/cypher"
query = "MATCH (m:E16_Measurement) RETURN m LIMIT 2"
results = requests.get(api, { "query": query }).json()

Query with JavaScript

const api = "https://graph.nfdi4objects.net/api/cypher"
const query = "MATCH (m:E16_Measurement) RETURN m LIMIT 2"
results = await fetch(api, { query }).then(res => res.json())

Query with curl

The Cypher query must be URL-escaped, this is done by using argument --data-urlencode:

curl -G https://graph.nfdi4objects.net/api/cypher --data-urlencode 'query=MATCH (m:E16_Measurement) RETURN m LIMIT 2'

The Cypher query can also be passed from a file:

curl -G https://graph.nfdi4objects.net/api/cypher --data-urlencode '[email protected]'

Linked Open Data

Information about collections, each identified by an URI starting with https://graph.nfdi4objects.net/collection/, can be retrieved as Linked Open Data (LOD) at path /collection in HTML and in RDF serializations. The data is retrieved via SPARQL API, so retrieving https://graph.nfdi4objects.net/collection/1 results in the same data as this SPARQL query from graph https://graph.nfdi4objects.net/collection/:

DESCRIBE <https://graph.nfdi4objects.net/collection/1> FROM <https://graph.nfdi4objects.net/collection/>

The RDF serialization is determined via HTTP Content Negotiation or with optional query parameter format.

Information about terminologies will be made available from https://graph.nfdi4objects.net/terminology/.

Development

To locally run the application first install required Python dependencies with virtualenv:

python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt

Then locally run for testing:

python app.py --help

Alternatively run make deps and make dev.

Please run make lint to detect Python coding style violations and make fix to fix some of these violations. Some unit tests are run with make test.

To populate the configured knowledge graph databases with actual data, see the source code repository https://github.com/nfdi4objects/n4o-import.

License

MIT License