super-sparql 0.1.0

This package is for parsing the SPARQL Queries in order to save the world

🧠 super_sparql

super_sparql is a lightweight Python package for parsing and analyzing SPARQL queries in a structured, programmable way. It extracts components like prefixes, triple patterns, filters, limits, and even infers types from the query structure.

🚀 Features

• Parses SPARQL SELECT, CONSTRUCT, ASK, DESCRIBE queries
• Extracts:
  • Prefixes
  • Triple patterns
  • SELECT variables
  • Filters
  • LIMIT / OFFSET / ORDER BY
• Infers variable roles and types
• Returns a structured dataclass representation of the query
• Handles partial or loosely formatted SPARQL

📦 Installation

pip install super_sparql

Or if you're using it locally (after building your wheel):

pip install dist/super_sparql-0.1.0-py3-none-any.whl

✨ Quick Start

from super_sparql import parse_my_SPARQL

query = """
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX dcm: <http://example.org/ns/dcm#>

SELECT ?x ?label
WHERE {
  ?x rdf:type dcm:Image .
  ?x dcm:label ?label .
  FILTER(lang(?label) = "en")
}
ORDER BY ?label
LIMIT 10
OFFSET 5
"""

parser = parse_my_SPARQL(query)
parsed = parser.parse()
print(parsed)

Output:

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX dcm: <http://example.org/ns/dcm#>

SELECT ?x ?label
WHERE {
  ?x rdf:type dcm:Image.
  ?x dcm:label ?label.
  FILTER(lang(?label) = "en")
}
ORDER BY ?label
LIMIT 10
OFFSET 5

🔍 Use Cases

1. Get SELECT Variables

parser.get_select_variables()
# ➞ ['?x', '?label']

2. Extract Triple Patterns

for triple in parser.get_triple_patterns():
    print(triple)
# ➞ ?x rdf:type dcm:Image
# ➞ ?x dcm:label ?label

3. Get Variable Types

parser.get_select_variable_types()
# ➞ {'?x': ['dcm:Image'], '?label': ['Range of dcm:label']}

4. Full Query Analysis

analysis = parser.analyze_query()
print(analysis)

Sample Output:

{
  "query_type": "SELECT",
  "select_variables": ["?x", "?label"],
  "variable_types": {
    "?x": ["dcm:Image"],
    "?label": ["Range of dcm:label"]
  },
  "variables_details": {
    "?x": {
      "in_select": true,
      "occurrences": {
        "as_subject": [{"triple_index": 0, "triple": "?x rdf:type dcm:Image"}, {"triple_index": 1, "triple": "?x dcm:label ?label"}],
        "as_predicate": [],
        "as_object": []
      }
    },
    ...
  },
  "triple_count": 2,
  "filter_count": 1,
  "has_order_by": true,
  "has_limit": true,
  "has_offset": true
}

📚 Supported SPARQL Clauses

Clause	Supported	Notes
PREFIX	✅	Auto-completes common prefixes
SELECT	✅	Supports variables and wildcard *
WHERE	✅	Parses triple patterns and filters
FILTER	✅	Basic extraction supported
ORDER BY	✅	Supports ASC/DESC
LIMIT	✅	Extracts integer limit
OFFSET	✅	Extracts integer offset
CONSTRUCT	⚠️	Detected, parsed like SELECT
ASK, DESCRIBE	⚠️	Detected, parsed like SELECT

📝 License

MIT License — see LICENSE for full text.

🙌 Acknowledgements

Built with sarcasm and care to make SPARQL parsing easier, faster, and less soul-crushing.

🧪 Example Query Playground

Try with queries like:

SELECT * 
WHERE {
  ?book rdf:type dcm:Book .
  ?book dcm:title ?title .
  FILTER regex(?title, "SPARQL", "i")
}
ORDER BY DESC(?title)
LIMIT 5