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RDF Graph Navigation for Dump Generation

Problem Statement

During development of RDF dump generation capabilities, we encountered a fundamental issue with translating complex SPARQL WHERE patterns into valid CONSTRUCT patterns. The original approach of using select_pattern for both SELECT and CONSTRUCT queries failed because:

SELECT/COUNT queries: WHERE clause defines what to match - complex patterns work fine
CONSTRUCT queries: CONSTRUCT clause defines what triples to OUTPUT, WHERE clause defines what to MATCH
The Issue: Using constraint patterns like ?pc gp:value "W2306" as CONSTRUCT templates doesn't make sense - you want to output DATA about found entities, not search constraints.

Original Question

"can you explain better - is this a SPARQL flaw/limitation or something we can improve?"
"no we have to do this systematically. I want a dump capability that follows a simple graph navigation idea. So i can imagine a sequence of navigation steps that possibly translate to basic graph patterns. In the end the sequence should return a subgraph that fits the navigation. E.g. in wikidata we navigate to Triplestore and then follow the instanceof path and then we want all properties. in gov we navigate to all nodes with a given W-num and then we want certain properties (i would also be happy with "all" properties for the time being". In gremlin this style is much simpler to state than in SPARQL so i imagine steps similar to gremlin steps which translate to Basic graph patterns. To make this queryable we could create a sequence of queries which should not be too troublesome since the intention is to work with subgraphs that have mostly less than 100000 nodes"

Proposed Solution: Systematic Graph Navigation

Core Design Concept

Replace complex SPARQL patterns with a systematic graph navigation approach inspired by Gremlin traversals. This separates navigation logic from SPARQL complexity and makes dump generation composable and maintainable.

Data Structures

@dataclass
class NavigationStep:
    """A single graph navigation step"""
    step_type: str  # "start", "follow", "properties"
    pattern: str    # Basic graph pattern for this step
    variable: str   # Output variable name

@dataclass 
class GraphNavigation:
    """Sequence of navigation steps"""
    steps: List[NavigationStep]
    
    def to_sparql_queries(self) -> List[str]:
        """Convert navigation steps to sequence of SPARQL queries"""
        # Step 1: Find starting nodes
        # Step 2: Follow relationships  
        # Step 3: Get properties
        pass

@dataclass
class RdfDataset:
    name: str
    endpoint_url: str
    navigation: GraphNavigation  # Replace select_pattern
    expected_triples: Optional[int] = None
    description: Optional[str] = None

Example Configurations

Wikidata Triplestores

wikidata_triplestores:
  name: "Wikidata Triplestores"
  endpoint_url: "https://query.wikidata.org/sparql"
  expected_triples: 1190
  description: "All triplestore instances and their properties"
  navigation:
    steps:
      - step_type: "start"
        pattern: "?instance wdt:P31 wd:Q3539533"
        variable: "instance"
      - step_type: "properties" 
        pattern: "?instance ?p ?o"
        variable: "instance"

GOV W2306 Postal Code

gov_w2306:
  name: "GOV W2306 Coordinates"
  endpoint_url: "https://gov-sparql.genealogy.net/dataset/sparql"
  expected_triples: 100
  description: "Places with postal code W2306 and their properties"
  navigation:
    steps:
      - step_type: "start"
        pattern: "?place gp:hasPostalCode ?pc . ?pc gp:value \"W2306\""
        variable: "place"
      - step_type: "properties"
        pattern: "?place ?p ?o"
        variable: "place"

Implementation Strategy

Step-wise Query Generation: Convert navigation steps into a sequence of SPARQL queries
Subgraph Assembly: Each step builds upon previous results to construct the target subgraph
Manageable Scope: Target subgraphs with <100,000 nodes for practical processing
Gremlin-like Semantics: Provide intuitive graph traversal patterns that translate to efficient SPARQL

Benefits

Separation of Concerns: Navigation logic separated from SPARQL syntax complexity
Composability: Steps can be combined and reused across different datasets
Maintainability: Clear, declarative navigation patterns
Debugging: Each step can be tested independently
Extensibility: New step types can be added as needed

Future Extensions

Filtering Steps: Add conditions to navigation steps
Aggregation Steps: Collect statistics during traversal
Branching Navigation: Support multiple paths from same starting point
Optimization: Combine steps into efficient compound queries where possible

Conclusion

This systematic approach transforms the RDF dump generation from a SPARQL pattern matching problem into an intuitive graph navigation problem. By modeling traversals as sequences of basic steps, we achieve both clarity and flexibility while maintaining the ability to generate efficient SPARQL queries for actual execution.

RDF Graph Navigation

Contents