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Creates a new node with the label 'dbo_Concepts' in a Neo4j graph database with the specified properties.

Purpose

This function is designed to insert a new concept node into a Neo4j database using the DBpedia Ontology (dbo) schema. It dynamically constructs a Cypher CREATE query based on the provided properties dictionary, executes the query, and returns the created node. This is useful for populating knowledge graphs with concept entities from structured data sources.

Source Code

def create_dbo_concepts(properties):
    """Create a new dbo_Concepts node"""
    props_list = ', '.join([f"n.{prop} = ${prop}" for prop in properties.keys()])
    query = f"""
    CREATE (n:dbo_Concepts)
    SET {props_list}
    RETURN n
    """
    result = run_query(query, properties)
    return result[0] if result else None

Parameters

Parameter Details

properties: A dictionary containing key-value pairs representing the properties to be set on the new dbo_Concepts node. Keys should be valid property names (strings) and values can be any data type supported by Neo4j (strings, numbers, booleans, lists, etc.). Example: {'name': 'Artificial Intelligence', 'category': 'Technology', 'year': 1956}

Return Value

Returns the created Neo4j node object (typically a dictionary-like object with node properties and metadata) if the creation was successful. Returns None if the query execution fails or returns no results. The returned node includes all properties that were set plus any default properties added by Neo4j (like internal ID).

Dependencies

• neo4j

Required Imports

from neo4j import GraphDatabase

Usage Example

# Assuming run_query function is defined and Neo4j is configured

# Example 1: Create a concept node with basic properties
properties = {
    'name': 'Machine Learning',
    'description': 'A subset of artificial intelligence',
    'category': 'Computer Science',
    'created_date': '2024-01-15'
}

node = create_dbo_concepts(properties)
if node:
    print(f"Created node: {node}")
else:
    print("Failed to create node")

# Example 2: Create a concept with multiple property types
properties = {
    'name': 'Neural Networks',
    'popularity_score': 95,
    'is_active': True,
    'related_topics': ['Deep Learning', 'AI', 'Data Science']
}

node = create_dbo_concepts(properties)
if node:
    print(f"Node created with ID: {node.get('id')}")

Best Practices

• Validate the properties dictionary before passing it to ensure all keys are valid property names (no special characters that could break the Cypher query)
• Be cautious of Cypher injection vulnerabilities - the function uses parameterized queries which is good, but ensure property keys come from trusted sources
• Handle the None return value appropriately in calling code to detect creation failures
• Consider adding error handling around the function call to catch Neo4j connection or query execution errors
• Ensure the run_query function properly handles database connections and transactions
• Use consistent property naming conventions across all dbo_Concepts nodes for easier querying
• Consider adding constraints or indexes on frequently queried properties in the Neo4j database
• The function does not check for duplicate nodes - implement uniqueness constraints at the database level if needed

Tags

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