# semantic_network Package
## SemanticNetwork
The class is designed for encoding graph structures using universal triplets:
entity_1 relation entity_2
entity_2 relation entity_3
- Triplet parts are separated by spaces
- First and third parts are objects
- Second part is a directed relationship between objects
#### 1. Creating and Extending the Graph:
from semantic_network import SemanticNetwork
script = """
Circle hasPart Circle.radius
Circle hasPart Circle.center
"""
sn = SemanticNetwork.from_script(script)
script_2 = """
c1 fromProto Circle
c1 hasPart c1.radius
c2 fromProto Circle
c2 hasPart c2.radius
c2 hasPart c2.center
c2.radius fromProto Circle.radius
"""
sn.append_script(script_2)
This creates a graph with objects and relationships:
print(sn)
----------------------------------------
|- c1
|- Circle
|- c1 fromProto Circle
|- c1.radius
|- c1 hasPart c1.radius
|- c2
|- c2 fromProto Circle
|- c2.radius
|- c2 hasPart c2.radius
|- c2.center
|- c2 hasPart c2.center
|- Circle.radius
|- c2.radius fromProto Circle.radius
----------------------------------------
#### 2. Graph Search
Search queries are specified using a special query format.
Elements starting with * are treated as variables, while other elements are treated as constants.
q = """
*o1 hasPart *o2
*o1 fromProto Circle
"""
for objs, rels in sn.query(q):
print(objs, rels)
Returns dictionaries with found graph fragments:
{'o1': 'c2', 'Circle': 'Circle', 'o2': 'c2.radius'} {'fromProto': 'fromProto', 'hasPart': 'hasPart'}
{'o1': 'c2', 'Circle': 'Circle', 'o2': 'c2.center'} {'fromProto': 'fromProto', 'hasPart': 'hasPart'}
{'o1': 'c1', 'Circle': 'Circle', 'o2': 'c1.radius'} {'fromProto': 'fromProto', 'hasPart': 'hasPart'}
During search, the system also verifies that the query graph is connected and acyclic.
#### 3. Utility Methods
# Extend the graph
sn.append_script(new_script)
# Print graph description (number of unique objects and relationships)
sn.describe()
# Create a copy
sn.copy()
# Serialization to dictionary and creation from dictionary
sn.to_dict()
sn = SemanticNetwork.from_dict(dict_data)
# Write to file with additional custom variables dictionary, and read from file
sm.dump(path, variables)
sn = SemanticNetwork.load(path)
Raw data
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"description": "# semantic_network Package\n## SemanticNetwork\n\nThe class is designed for encoding graph structures using universal triplets:\n\n entity_1 relation entity_2\n entity_2 relation entity_3\n\n- Triplet parts are separated by spaces\n- First and third parts are objects\n- Second part is a directed relationship between objects\n\n\n#### 1. Creating and Extending the Graph:\n\n from semantic_network import SemanticNetwork\n\n script = \"\"\"\n Circle hasPart Circle.radius\n Circle hasPart Circle.center\n \"\"\"\n\n sn = SemanticNetwork.from_script(script)\n\n script_2 = \"\"\"\n c1 fromProto Circle\n c1 hasPart c1.radius\n c2 fromProto Circle\n c2 hasPart c2.radius\n c2 hasPart c2.center\n c2.radius fromProto Circle.radius\n \"\"\"\n\n sn.append_script(script_2)\n\nThis creates a graph with objects and relationships:\n\n print(sn)\n ----------------------------------------\n |- c1\n |- Circle\n |- c1 fromProto Circle\n |- c1.radius\n |- c1 hasPart c1.radius\n |- c2\n |- c2 fromProto Circle\n |- c2.radius\n |- c2 hasPart c2.radius\n |- c2.center\n |- c2 hasPart c2.center\n |- Circle.radius\n |- c2.radius fromProto Circle.radius\n ----------------------------------------\n\n\n#### 2. Graph Search\nSearch queries are specified using a special query format.\nElements starting with * are treated as variables, while other elements are treated as constants.\n\n q = \"\"\"\n *o1 hasPart *o2\n *o1 fromProto Circle\n \"\"\"\n\n for objs, rels in sn.query(q):\n print(objs, rels)\n\nReturns dictionaries with found graph fragments:\n\n {'o1': 'c2', 'Circle': 'Circle', 'o2': 'c2.radius'} {'fromProto': 'fromProto', 'hasPart': 'hasPart'}\n {'o1': 'c2', 'Circle': 'Circle', 'o2': 'c2.center'} {'fromProto': 'fromProto', 'hasPart': 'hasPart'}\n {'o1': 'c1', 'Circle': 'Circle', 'o2': 'c1.radius'} {'fromProto': 'fromProto', 'hasPart': 'hasPart'}\n\nDuring search, the system also verifies that the query graph is connected and acyclic.\n\n\n#### 3. Utility Methods\n\n # Extend the graph\n sn.append_script(new_script)\n\n # Print graph description (number of unique objects and relationships)\n sn.describe()\n \n # Create a copy\n sn.copy()\n\n # Serialization to dictionary and creation from dictionary\n sn.to_dict()\n sn = SemanticNetwork.from_dict(dict_data)\n\n # Write to file with additional custom variables dictionary, and read from file\n sm.dump(path, variables)\n sn = SemanticNetwork.load(path)\n",
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