RDF/OWL Metadata Automation: Dynamic Knowledge Graphs with Friend-of-a-Friend Associations¶

# RDF/OWL implementation with Python
import rdflib
from rdflib import Graph, Namespace, Literal, URIRef
from rdflib.namespace import RDF, RDFS, OWL, FOAF
import owlrl

class RDFOWLAutomation:
    def __init__(self):
        self.graph = Graph()
        self.setup_namespaces()
        self.setup_reasoning()

    def setup_namespaces(self):
        # Define custom namespaces
        self.EX = Namespace("http://example.org/ontology#")
        self.graph.bind("ex", self.EX)
        self.graph.bind("foaf", FOAF)
        self.graph.bind("owl", OWL)

    def setup_reasoning(self):
        # Configure OWL reasoning
        self.reasoner = owlrl.OWLRL_Semantics(self.graph)
        self.reasoner.loadOntology()

    def add_person(self, person_uri, name, email):
        # Add person with properties
        person = URIRef(person_uri)
        self.graph.add((person, RDF.type, FOAF.Person))
        self.graph.add((person, FOAF.name, Literal(name)))
        self.graph.add((person, FOAF.mbox, Literal(email)))

    def add_knows_relationship(self, person1_uri, person2_uri):
        # Add knows relationship
        person1 = URIRef(person1_uri)
        person2 = URIRef(person2_uri)
        self.graph.add((person1, FOAF.knows, person2))

    def infer_relationships(self):
        # Run reasoning to infer new relationships
        self.reasoner.reason()

    def query_friends_of_friends(self, person_uri):
        # SPARQL query for friends of friends
        query = """
        PREFIX foaf: <http://xmlns.com/foaf/0.1/>
        SELECT ?friendOfFriend
        WHERE {
            ?person foaf:knows ?friend .
            ?friend foaf:knows ?friendOfFriend .
            FILTER(?person = <""" + person_uri + """>)
            FILTER(?person != ?friendOfFriend)
        }
        """
        return self.graph.query(query)

# Automated metadata generation
class MetadataAutomation:
    def __init__(self, graph):
        self.graph = graph
        self.reasoner = owlrl.OWLRL_Semantics(graph)

    def generate_colleague_relationships(self):
        # Generate colleague relationships based on project membership
        query = """
        PREFIX : <http://example.org/ontology#>
        SELECT ?person1 ?person2
        WHERE {
            ?person1 :worksOn ?project .
            ?person2 :worksOn ?project .
            FILTER(?person1 != ?person2)
        }
        """

        results = self.graph.query(query)
        for row in results:
            person1, person2 = row
            self.graph.add((person1, self.EX.hasColleague, person2))

    def generate_collaboration_suggestions(self):
        # Generate collaboration suggestions based on expertise
        query = """
        PREFIX : <http://example.org/ontology#>
        SELECT ?person1 ?person2 ?domain
        WHERE {
            ?person1 :hasExpertise ?domain .
            ?person2 :hasExpertise ?domain .
            FILTER(?person1 != ?person2)
            FILTER NOT EXISTS {
                ?person1 :hasCollaborated ?person2
            }
        }
        """

        results = self.graph.query(query)
        for row in results:
            person1, person2, domain = row
            self.graph.add((person1, self.EX.hasPotentialCollaborator, person2))

    def generate_document_recommendations(self):
        # Generate document recommendations based on network
        query = """
        PREFIX : <http://example.org/ontology#>
        PREFIX foaf: <http://xmlns.com/foaf/0.1/>
        SELECT ?person ?document ?connectionType
        WHERE {
            {
                ?person foaf:knows ?author .
                ?author :authoredBy ?document .
                BIND("friend" AS ?connectionType)
            } UNION {
                ?person :hasColleague ?colleague .
                ?colleague :authoredBy ?document .
                BIND("colleague" AS ?connectionType)
            }
        }
        """

        return self.graph.query(query)

# Performance optimization for large knowledge graphs
class OptimizedRDFOWL:
    def __init__(self):
        self.graph = Graph()
        self.indexes = {}
        self.cache = {}

    def build_indexes(self):
        # Build indexes for common queries
        self.indexes['knows'] = {}
        self.indexes['worksOn'] = {}
        self.indexes['authoredBy'] = {}

        for s, p, o in self.graph:
            if p == FOAF.knows:
                if s not in self.indexes['knows']:
                    self.indexes['knows'][s] = []
                self.indexes['knows'][s].append(o)
            elif p == self.EX.worksOn:
                if s not in self.indexes['worksOn']:
                    self.indexes['worksOn'][s] = []
                self.indexes['worksOn'][s].append(o)
            elif p == self.EX.authoredBy:
                if o not in self.indexes['authoredBy']:
                    self.indexes['authoredBy'][o] = []
                self.indexes['authoredBy'][o].append(s)

    def cached_reasoning(self, query):
        # Cache reasoning results
        cache_key = hash(query)
        if cache_key in self.cache:
            return self.cache[cache_key]

        result = self.graph.query(query)
        self.cache[cache_key] = result
        return result

    def batch_operations(self, operations):
        # Batch multiple operations for efficiency
        for operation in operations:
            if operation['type'] == 'add':
                self.graph.add((operation['subject'], operation['predicate'], operation['object']))
            elif operation['type'] == 'remove':
                self.graph.remove((operation['subject'], operation['predicate'], operation['object']))

# ❌ WRONG: Inefficient reasoning
def bad_reasoning():
    # Running reasoning on every query
    for query in queries:
        reasoner.reason()  # Expensive!
        results = graph.query(query)
    return results

# ✅ CORRECT: Efficient reasoning
def good_reasoning():
    # Run reasoning once, then query
    reasoner.reason()
    results = []
    for query in queries:
        results.append(graph.query(query))
    return results

# ❌ WRONG: No data validation
def add_person(name, email):
    graph.add((person, FOAF.name, Literal(name)))
    graph.add((person, FOAF.mbox, Literal(email)))
    # No validation!

# ✅ CORRECT: Data validation
def add_person(name, email):
    if not validate_email(email):
        raise ValueError("Invalid email format")
    if not validate_name(name):
        raise ValueError("Invalid name format")

    graph.add((person, FOAF.name, Literal(name)))
    graph.add((person, FOAF.mbox, Literal(email)))

# ✅ DO: Complete ontological design checklist
def ontological_design_checklist():
    checklist = {
        "class_design": [
            "Define clear class hierarchies",
            "Use appropriate subclass relationships",
            "Avoid circular dependencies",
            "Include class descriptions and comments"
        ],
        "property_design": [
            "Define domain and range for all properties",
            "Use appropriate property characteristics",
            "Define inverse properties where needed",
            "Use property chains for complex relationships"
        ],
        "reasoning_design": [
            "Design for automated reasoning",
            "Use transitive and symmetric properties",
            "Define functional and inverse functional properties",
            "Test reasoning performance"
        ],
        "data_quality": [
            "Validate data consistency",
            "Check for satisfiability",
            "Monitor reasoning performance",
            "Implement data provenance"
        ]
    }
    return checklist

# ✅ DO: Implementation best practices
class RDFOWLBestPractices:
    @staticmethod
    def validate_ontology(ontology):
        # Validate ontology consistency
        try:
            reasoner = owlrl.OWLRL_Semantics(ontology)
            reasoner.loadOntology()
            return True
        except Exception as e:
            print(f"Ontology validation failed: {e}")
            return False

    @staticmethod
    def optimize_queries(queries):
        # Optimize SPARQL queries
        optimized_queries = []
        for query in queries:
            # Add query optimization logic
            optimized_query = query.replace("SELECT *", "SELECT ?s ?p ?o")
            optimized_queries.append(optimized_query)
        return optimized_queries

    @staticmethod
    def monitor_performance(graph):
        # Monitor reasoning performance
        import time
        start_time = time.time()

        # Run reasoning
        reasoner = owlrl.OWLRL_Semantics(graph)
        reasoner.loadOntology()

        end_time = time.time()
        reasoning_time = end_time - start_time

        if reasoning_time > 10:  # 10 second threshold
            print(f"WARNING: Reasoning took {reasoning_time:.2f} seconds")

        return reasoning_time

# Basic RDF/OWL setup
import rdflib
from rdflib import Graph, Namespace, Literal, URIRef
from rdflib.namespace import RDF, RDFS, OWL, FOAF
import owlrl

# Create graph
graph = Graph()

# Define namespaces
EX = Namespace("http://example.org/ontology#")
graph.bind("ex", EX)
graph.bind("foaf", FOAF)

# Add data
alice = URIRef("http://example.org/alice")
bob = URIRef("http://example.org/bob")

graph.add((alice, RDF.type, FOAF.Person))
graph.add((alice, FOAF.name, Literal("Alice")))
graph.add((alice, FOAF.knows, bob))

# Run reasoning
reasoner = owlrl.OWLRL_Semantics(graph)
reasoner.loadOntology()

# Automated relationship discovery
def discover_relationships(graph):
    # Find transitive relationships
    query = """
    PREFIX foaf: <http://xmlns.com/foaf/0.1/>
    SELECT ?person ?indirectFriend
    WHERE {
        ?person foaf:knows+ ?indirectFriend .
        FILTER(?person != ?indirectFriend)
    }
    """
    return graph.query(query)