Update src/visualization.py

src/visualization.py

@@ -182,15 +182,17 @@ def analyze_query_ontology_concepts(query: str, ontology_manager) -> Tuple[List[
     Analyze the query to identify ontology concepts with confidence scores.
     This is a simplified implementation that would be replaced with NLP in production.
     """
-    query_lower = query.lower().split()
+    query_lower = query.lower()
     
     # Entity detection
     entity_mentions = []
     classes = ontology_manager.get_classes()
     
     for class_name in classes:
-        # Simple token matching (would use NER in production)
-        if class_name.lower() in query_lower:
+        # Use word boundary regex to improve matching
+        import re
+        pattern = r'\b' + re.escape(class_name.lower()) + r'\b'
+        if re.search(pattern, query_lower):
             # Get class info
             class_info = ontology_manager.ontology_data["classes"].get(class_name, {})
             
@@ -204,15 +206,16 @@ def analyze_query_ontology_concepts(query: str, ontology_manager) -> Tuple[List[
                 "description": class_info.get("description", "")
             })
     
-    # Relationship detection
+    # Similar improvement for relationship detection
     relationship_mentions = []
     relationships = ontology_manager.ontology_data.get("relationships", [])
     
     for rel in relationships:
         rel_name = rel["name"]
         
-        # Simple token matching
-        if rel_name.lower() in query_lower:
+        # Use word boundary regex
+        pattern = r'\b' + re.escape(rel_name.lower()) + r'\b'
+        if re.search(pattern, query_lower):
             # Assign confidence
             confidence = min(0.9, 0.5 + (len(rel_name) / 20))