diff --git a/tenet/scheme/amr_rule/__init__.py b/tenet/scheme/amr_rule/__init__.py
index 5b457376e0b9b7596c305507b83b210ac05b8b34..a1d62cb4b8a31f4f91bf42593bc7f7d308107a25 100644
--- a/tenet/scheme/amr_rule/__init__.py
+++ b/tenet/scheme/amr_rule/__init__.py
@@ -1,9 +1,11 @@
from scheme.amr_rule.preprocessing.amr_reification import *
from scheme.amr_rule.preprocessing.amrld_correcting import *
-from scheme.amr_rule.transduction.atomic_class_extractor import *
-from scheme.amr_rule.transduction.atomic_individual_extractor import *
-from scheme.amr_rule.transduction.atomic_property_extractor import *
+from scheme.amr_rule.transduction.atom_class_extractor import *
+from scheme.amr_rule.transduction.atom_individual_extractor import *
+from scheme.amr_rule.transduction.atom_property_extractor import *
+from scheme.amr_rule.transduction.atom_value_extractor import *
+from scheme.amr_rule.transduction.atom_phenomena_extractor import *
from scheme.amr_rule.transduction.composite_class_extractor_1 import *
diff --git a/tenet/scheme/amr_rule/transduction/atomic_class_extractor.py b/tenet/scheme/amr_rule/transduction/atom_class_extractor.py
similarity index 87%
rename from tenet/scheme/amr_rule/transduction/atomic_class_extractor.py
rename to tenet/scheme/amr_rule/transduction/atom_class_extractor.py
index b5b52f205df6eb19eafb4a8869f359bafbea65a3..b1b4c07ca991857f5d5a3785086aed9519b205cb 100644
--- a/tenet/scheme/amr_rule/transduction/atomic_class_extractor.py
+++ b/tenet/scheme/amr_rule/transduction/atom_class_extractor.py
@@ -17,24 +17,18 @@ from transduction.naming_computer import define_composite_naming_1, define_restr
#==============================================================================
-# Select Pattern: leaf{variable, concept}
+# Pattern Search: leaf{variable, concept}
#==============================================================================
-
-def __rule_pattern_query_code(graph):
- select_data_list = ['?leaf', '?conceptName']
- clause_list = []
- clause_list.append(f'?leaf a amr:AMR_Leaf.')
- clause_list.append(f'?leaf amr:hasConcept ?leafConcept.')
- clause_list.append(f'?leafConcept rdfs:subClassOf amr:AMR_Term_Concept.')
- clause_list.append(f'?leafConcept amr:label ?conceptName.')
- query_code = generate_select_query(graph, select_data_list, clause_list)
- return query_code
-
def __search_pattern(graph):
- query_code = __rule_pattern_query_code(graph)
- rule_pattern_set = graph.query(query_code)
- return rule_pattern_set
+ select_data_list = ['?leaf', '?conceptName']
+ clause_list = [f'?leaf a amr:AMR_Leaf.',
+ f'?leaf amr:hasConcept ?leafConcept.',
+ f'?leafConcept rdfs:subClassOf amr:AMR_Term_Concept.',
+ f'?leafConcept amr:label ?conceptName.']
+ query_code = generate_select_query(graph, select_data_list, clause_list)
+ result_set = graph.query(query_code)
+ return query_code, result_set
#==============================================================================
@@ -134,17 +128,17 @@ def __construct_atom_class_net(graph, leaf, concept_name):
# Main Method
#==============================================================================
-def extract_atomic_class(graph):
+def extract_atom_class(graph):
# -- Rule Initialization
- rule_label = 'extract atomic classes [ leaf{variable, concept} ]'
+ rule_label = 'extract atom classes'
# -- Search for patterns
- rule_pattern_set = __search_pattern(graph)
+ _, pattern_set = __search_pattern(graph)
# -- Selection Analyzing (1)
rule_triple_list = []
- for pattern in rule_pattern_set:
+ for pattern in pattern_set:
# -- New Net Construction (from 3 nets)
new_class, triple_list = __construct_atom_class_net(
diff --git a/tenet/scheme/amr_rule/transduction/atom_individual_extractor.py b/tenet/scheme/amr_rule/transduction/atom_individual_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fcf81ac3cd4d7477fd857ae2e7c309de863bdf1
--- /dev/null
+++ b/tenet/scheme/amr_rule/transduction/atom_individual_extractor.py
@@ -0,0 +1,172 @@
+#!/usr/bin/python3.10
+# -*-coding:Utf-8 -*
+
+#==============================================================================
+# TENET: Rule to extract atomic individuals
+#------------------------------------------------------------------------------
+# Net Expansion AMR rule to extract atomic individuals
+# Rule: leaf{variable, concept} => atomicClass
+#==============================================================================
+
+from rdflib import Graph
+
+import transduction
+from transduction import net
+from transduction.query_builder import generate_select_query
+from transduction.naming_computer import define_composite_naming_1, define_restriction_naming
+
+
+#==============================================================================
+# Pattern Search: Leaf{variable, concept} corresponding to individual
+#==============================================================================
+
+def __search_pattern(graph):
+ select_data_list = ['?baseLeaf', '?valueLabel', '?classNet']
+ clause_list = [f'?baseLeaf a amr:AMR_Leaf.',
+ f'?baseLeaf amr:hasConcept ?leafConcept.',
+ f'?baseLeaf ?edge ?value.',
+ f'?edge amr:hasRoleID ?roleID.',
+ f'VALUES ?roleID {{"name"}}',
+ f'?value a amr:AMR_Value.',
+ f'?value rdfs:label ?valueLabel.',
+ f'?classNet a [rdfs:subClassOf* net:Class_Net].',
+ f'?classNet net:coverBaseNode ?baseLeaf.']
+ query_code = generate_select_query(graph, select_data_list, clause_list)
+ result_set = graph.query(query_code)
+ return query_code, result_set
+
+
+#==============================================================================
+# Useful Additional Search
+#==============================================================================
+
+def __structure_query_code(graph):
+ select_data_list = ['?sentenceId']
+ clause_list = []
+ clause_list.append(f'?root a amr:AMR_Root.')
+ clause_list.append(f'?root amr:hasSentenceID ?sentenceId.')
+ query_code = generate_select_query(graph, select_data_list, clause_list)
+ return query_code
+
+
+def __search_structure(graph):
+ query_code = __structure_query_code(graph)
+ result_set = graph.query(query_code)
+ return result_set
+
+
+def __search_leaf_in_relation(graph, base_leaf_uri):
+ select_data_list = ['?inRelationRole', '?inNet']
+ clause_list = [f'?inNet a [rdfs:subClassOf* net:Net].',
+ f'?inNet net:coverBaseNode ?inLeaf.',
+ ('?inLeaf', '?relation', base_leaf_uri),
+ f'?relation amr:hasAmrRole ?inRelationRole.']
+ query_code = generate_select_query(graph, select_data_list, clause_list)
+ result_set = graph.query(query_code)
+ return query_code, result_set
+
+
+def __search_leaf_out_relation(graph, base_leaf_uri):
+ select_data_list = ['?outRelationRole', '?outNet']
+ clause_list = [f'?outNet a [rdfs:subClassOf* net:Net].',
+ f'?outNet net:coverBaseNode ?outLeaf.',
+ (base_leaf_uri, '?relation', '?outLeaf'),
+ f'?relation amr:hasAmrRole ?outRelationRole.']
+ query_code = generate_select_query(graph, select_data_list, clause_list)
+ result_set = graph.query(query_code)
+ return query_code, result_set
+
+
+
+#==============================================================================
+# Useful Computation Method(s)
+#==============================================================================
+
+def __get_structure(graph):
+ structure = 'unknown'
+ structure_set = __search_structure(graph)
+ for row in structure_set:
+ structure = row.sentenceId
+ return structure
+
+
+def __define_naming(value_label):
+ value_label = value_label.replace(' ', '#')
+ value_label = value_label.replace('-', '#')
+ word_set = value_label.split('#')
+ first = True
+ for word in word_set:
+ if first:
+ naming = word
+ first = False
+ else:
+ naming += word.capitalize()
+ return naming
+
+
+def __propagate_relation(graph, target_net, base_leaf):
+ _, in_relation_set = __search_leaf_in_relation(graph, base_leaf)
+ for row in in_relation_set:
+ target_net.input_relation_list += [(row.inNet, row.inRelationRole, _)]
+ _, out_relation_set = __search_leaf_out_relation(graph, base_leaf)
+ for row in out_relation_set:
+ target_net.output_relation_list += [(_, row.outRelationRole, row.outNet)]
+
+
+
+#==============================================================================
+# Construct Method(s)
+#==============================================================================
+
+def __construct_individual_net(
+ graph, base_leaf, value_label, mother_class_net):
+
+ # -- Net Composition
+ individual_net = net.IndividualNet(graph)
+
+ # -- Data Computation
+ individual_net.base_node = base_leaf
+ individual_net.node = base_leaf
+ individual_net.individual_label = value_label
+ individual_net.mother_class_net = mother_class_net
+ individual_net.structure = __get_structure(graph)
+
+ # -- Net Naming
+ individual_net.naming = __define_naming(value_label)
+
+ # -- Relation Propagation
+ __propagate_relation(graph, individual_net, base_leaf)
+
+ # -- Finalization
+ individual_net.finalize()
+ triple_definition = individual_net.generate_triple_definition()
+
+ return individual_net, triple_definition
+
+
+
+#==============================================================================
+# Main Method
+#==============================================================================
+
+def extract_atom_individual(graph):
+
+ # -- Rule Initialization
+ rule_label = 'extract atom individuals'
+
+ # -- Search for patterns
+ _, pattern_set = __search_pattern(graph)
+
+ # -- Selection Analyzing (1)
+ rule_triple_list = []
+ for pattern in pattern_set:
+
+ # -- New Net Construction (from 3 nets)
+ new_class, triple_list = __construct_individual_net(
+ graph, pattern.baseLeaf, pattern.valueLabel, pattern.classNet)
+
+ # -- Resulting List Update
+ # class_net_list.append(new_class)
+ rule_triple_list += triple_list
+
+ return rule_label, rule_triple_list
\ No newline at end of file
diff --git a/tenet/scheme/amr_rule/transduction/atomic_individual_extractor.py b/tenet/scheme/amr_rule/transduction/atom_phenomena_extractor.py
similarity index 83%
rename from tenet/scheme/amr_rule/transduction/atomic_individual_extractor.py
rename to tenet/scheme/amr_rule/transduction/atom_phenomena_extractor.py
index e6bf8bbd4379c5aa8abffb9bc78dba52beb5729a..7a744ebcc7c14c0093adc62dcd2306831c6f4235 100644
--- a/tenet/scheme/amr_rule/transduction/atomic_individual_extractor.py
+++ b/tenet/scheme/amr_rule/transduction/atom_phenomena_extractor.py
@@ -20,27 +20,22 @@ from transduction.naming_computer import define_composite_naming_1, define_restr
# Pattern Search: Leaf{variable, concept} corresponding to individual
#==============================================================================
-def __rule_pattern_query_code(graph):
- select_data_list = ['?baseLeaf', '?conceptName', '?valueLabel', '?classNet']
- clause_list = []
- clause_list.append(f'?baseLeaf a amr:AMR_Leaf.')
- clause_list.append(f'?baseLeaf amr:hasConcept ?leafConcept.')
- clause_list.append(f'?baseLeaf ?edge ?value.')
- clause_list.append(f'?edge amr:hasRoleID ?roleID.')
- clause_list.append(f'VALUES ?roleID {{"name"}}')
- clause_list.append(f'?value a amr:AMR_Value.')
- clause_list.append(f'?value rdfs:label ?valueLabel.')
- clause_list.append(f'?classNet a [rdfs:subClassOf* net:Class_Net].')
- clause_list.append(f'?classNet net:coverBaseNode ?baseLeaf.')
- clause_list.append(f'?classNet net:hasClassName ?conceptName.')
- query_code = generate_select_query(graph, select_data_list, clause_list)
- return query_code
-
-
def __search_pattern(graph):
- query_code = __rule_pattern_query_code(graph)
- rule_pattern_set = graph.query(query_code)
- return rule_pattern_set
+ select_data_list = ['?baseLeaf', '?phenomenaLabel', '?relationConceptPhenomena', '?relationConceptLabel']
+ clause_list = [f'?baseLeaf a amr:AMR_Leaf.',
+ f'?baseLeaf amr:hasConcept ?concept.',
+ f'?baseLeaf amr:hasVariable ?variable.',
+ f'?variable amr:label ?varLabel.',
+ f'?concept rdfs:subClassOf amr:AMR_Relation_Concept.',
+ f'?concept amr:hasPhenomenaLink ?relationConceptPhenomena.',
+ f'?concept amr:label ?relationConceptLabel.',
+ f'?relationConceptPhenomena amr:label ?phenomenaLabel.',
+ f'?baseLeaf ?relation ?argLeaf.',
+ f'?relation amr:hasAmrRole ?someArgRole.']
+ query_code = generate_select_query(graph, select_data_list, clause_list)
+ result_set = graph.query(query_code)
+ return query_code, result_set
+
#==============================================================================
@@ -156,17 +151,17 @@ def __construct_individual_net(
# Main Method
#==============================================================================
-def extract_atomic_individual(graph):
+def extract_atom_phenomena(graph):
# -- Rule Initialization
- rule_label = 'extract atomic individuals [ leaf{variable, concept} ]'
+ rule_label = 'extract atom phenomena'
# -- Search for patterns
- rule_pattern_set = __search_pattern(graph)
+ _, pattern_set = __search_pattern(graph)
# -- Selection Analyzing (1)
rule_triple_list = []
- for pattern in rule_pattern_set:
+ for pattern in pattern_set:
# -- New Net Construction (from 3 nets)
new_class, triple_list = __construct_individual_net(
diff --git a/tenet/scheme/amr_rule/transduction/atomic_property_extractor.py b/tenet/scheme/amr_rule/transduction/atom_property_extractor.py
similarity index 90%
rename from tenet/scheme/amr_rule/transduction/atomic_property_extractor.py
rename to tenet/scheme/amr_rule/transduction/atom_property_extractor.py
index 3304ed06cb86c0d7557f60b3376207ee47a6e2ff..f01e638b4114df8e414db23f60559ad487de44fc 100644
--- a/tenet/scheme/amr_rule/transduction/atomic_property_extractor.py
+++ b/tenet/scheme/amr_rule/transduction/atom_property_extractor.py
@@ -21,21 +21,15 @@ from transduction.naming_computer import define_composite_naming_1, define_restr
# Pattern Search: Leaf{variable, amrPrecicateConcept}
#==============================================================================
-def __rule_pattern_query_code(graph):
+def __search_pattern(graph):
select_data_list = ['?baseLeaf', '?predicateLabel']
- clause_list = []
- clause_list.append(f'?baseLeaf a amr:AMR_Leaf.')
- clause_list.append(f'?baseLeaf amr:hasConcept ?leafConcept.')
- clause_list.append(f'?leafConcept rdfs:subClassOf amr:AMR_Predicat_Concept.')
- clause_list.append(f'?leafConcept amr:label ?predicateLabel.')
+ clause_list = [f'?baseLeaf a amr:AMR_Leaf.',
+ f'?baseLeaf amr:hasConcept ?leafConcept.',
+ f'?leafConcept rdfs:subClassOf amr:AMR_Predicat_Concept.',
+ f'?leafConcept amr:label ?predicateLabel.']
query_code = generate_select_query(graph, select_data_list, clause_list)
- return query_code
-
-
-def __search_pattern(graph):
- query_code = __rule_pattern_query_code(graph)
result_set = graph.query(query_code)
- return result_set
+ return query_code, result_set
#==============================================================================
@@ -168,17 +162,17 @@ def __construct_atom_property_net(graph, base_leaf, predicate_label):
# Main Method
#==============================================================================
-def extract_atomic_property(graph):
+def extract_atom_property(graph):
# -- Rule Initialization
- rule_label = 'extract atomic properties [ leaf{variable, amrTermConcept} ]'
+ rule_label = 'extract atomic properties'
# -- Search for patterns
- rule_pattern_set = __search_pattern(graph)
+ _, pattern_set = __search_pattern(graph)
# -- Selection Analyzing (1)
rule_triple_list = []
- for pattern in rule_pattern_set:
+ for pattern in pattern_set:
# -- New Net Construction (from 3 nets)
new_net, triple_list = __construct_atom_property_net(
diff --git a/tenet/scheme/amr_rule/transduction/atom_value_extractor.py b/tenet/scheme/amr_rule/transduction/atom_value_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bed1cb88082aaf540796a29a84572c653698803
--- /dev/null
+++ b/tenet/scheme/amr_rule/transduction/atom_value_extractor.py
@@ -0,0 +1,149 @@
+#!/usr/bin/python3.10
+# -*-coding:Utf-8 -*
+
+#==============================================================================
+# TENET: Rule to extract atomic individuals
+#------------------------------------------------------------------------------
+# Net Expansion AMR rule to extract atomic individuals
+# Rule: leaf{variable, concept} => atomicClass
+#==============================================================================
+
+from rdflib import Graph
+
+import transduction
+from transduction import net
+from transduction.query_builder import generate_select_query
+from transduction.naming_computer import define_composite_naming_1, define_restriction_naming
+
+
+#==============================================================================
+# Pattern Search: Leaf{variable, concept} corresponding to individual
+#==============================================================================
+
+def __search_pattern(graph):
+ select_data_list = ['?valueLabel']
+ clause_list = [f'?value a amr:AMR_Value.',
+ f'?value rdfs:label ?valueLabel.']
+ query_code = generate_select_query(graph, select_data_list, clause_list)
+ result_set = graph.query(query_code)
+ return query_code, result_set
+
+
+#==============================================================================
+# Useful Additional Search
+#==============================================================================
+
+def __structure_query_code(graph):
+ select_data_list = ['?sentenceId']
+ clause_list = []
+ clause_list.append(f'?root a amr:AMR_Root.')
+ clause_list.append(f'?root amr:hasSentenceID ?sentenceId.')
+ query_code = generate_select_query(graph, select_data_list, clause_list)
+ return query_code
+
+
+def __search_structure(graph):
+ query_code = __structure_query_code(graph)
+ result_set = graph.query(query_code)
+ return result_set
+
+
+def __search_leaf_in_relation(graph, base_leaf_uri):
+ select_data_list = ['?inRelationRole', '?inNet']
+ clause_list = [f'?inNet a [rdfs:subClassOf* net:Net].',
+ f'?inNet net:coverBaseNode ?inLeaf.',
+ ('?inLeaf', '?relation', base_leaf_uri),
+ f'?relation amr:hasAmrRole ?inRelationRole.']
+ query_code = generate_select_query(graph, select_data_list, clause_list)
+ result_set = graph.query(query_code)
+ return query_code, result_set
+
+
+def __search_leaf_out_relation(graph, base_leaf_uri):
+ select_data_list = ['?outRelationRole', '?outNet']
+ clause_list = [f'?outNet a [rdfs:subClassOf* net:Net].',
+ f'?outNet net:coverBaseNode ?outLeaf.',
+ (base_leaf_uri, '?relation', '?outLeaf'),
+ f'?relation amr:hasAmrRole ?outRelationRole.']
+ query_code = generate_select_query(graph, select_data_list, clause_list)
+ result_set = graph.query(query_code)
+ return query_code, result_set
+
+
+
+#==============================================================================
+# Useful Computation Method(s)
+#==============================================================================
+
+def __get_structure(graph):
+ structure = 'unknown'
+ structure_set = __search_structure(graph)
+ for row in structure_set:
+ structure = row.sentenceId
+ return structure
+
+
+def __define_naming(value_label):
+ value_label = value_label.replace(' ', '#')
+ value_label = value_label.replace('-', '#')
+ word_set = value_label.split('#')
+ first = True
+ for word in word_set:
+ if first:
+ naming = word
+ first = False
+ else:
+ naming += word.capitalize()
+ return naming
+
+
+
+#==============================================================================
+# Construct Method(s)
+#==============================================================================
+
+def __construct_value_net(
+ graph, value_label):
+
+ # -- Net Composition
+ value_net = net.ValueNet(graph)
+
+ # -- Data Computation
+ value_net.value_label = value_label
+ value_net.structure = __get_structure(graph)
+
+ # -- Net Naming
+ value_net.naming = __define_naming(value_label)
+
+ # -- Finalization
+ value_net.finalize()
+ triple_definition = value_net.generate_triple_definition()
+
+ return value_net, triple_definition
+
+
+
+#==============================================================================
+# Main Method
+#==============================================================================
+
+def extract_atom_value(graph):
+
+ # -- Rule Initialization
+ rule_label = 'extract atom values'
+
+ # -- Search for patterns
+ _, pattern_set = __search_pattern(graph)
+
+ # -- Selection Analyzing (1)
+ rule_triple_list = []
+ for pattern in pattern_set:
+
+ # -- New Net Construction (from 3 nets)
+ new_class, triple_list = __construct_value_net(graph, pattern.valueLabel)
+
+ # -- Resulting List Update
+ # class_net_list.append(new_class)
+ rule_triple_list += triple_list
+
+ return rule_label, rule_triple_list
\ No newline at end of file
diff --git a/tenet/scheme/amr_scheme_1.py b/tenet/scheme/amr_scheme_1.py
index 535bb44e0f5503c8c9b5559a0cf3e04c930dfe1d..4ce4dd22864ea85aa1cf738b4f6b8c5a1a34da88 100644
--- a/tenet/scheme/amr_scheme_1.py
+++ b/tenet/scheme/amr_scheme_1.py
@@ -227,9 +227,11 @@ classification_sequence = {
}
atomic_extraction_sequence = ['atomic-extraction-sequence',
- rule.extract_atomic_class,
- rule.extract_atomic_individual,
- rule.extract_atomic_property]
+ rule.extract_atom_class,
+ rule.extract_atom_individual,
+ rule.extract_atom_property,
+ rule.extract_atom_value,
+ rule.extract_atom_phenomena]
phenomena_polarity_analyze_sequence = ['phenomena_polarity_analyze_sequence',
rule.analyze_phenomena_polarity_1]
diff --git a/tenet/transduction/net/net.py b/tenet/transduction/net/net.py
index b1ab0264d6f796d6e16b28ad346cf1230b87d542..495735a4d7ecf8c3efe4678610da43a4a754d4ce 100644
--- a/tenet/transduction/net/net.py
+++ b/tenet/transduction/net/net.py
@@ -353,9 +353,9 @@ class Net:
# -- Net Node Reference
if self.base_node is None:
- net_node_ref = f'blankNode1'
+ net_node_ref = f'blankNode'
elif len(self.base_node) <= 0:
- net_node_ref = f'blankNode2'
+ net_node_ref = f'blankNode'
else:
assert len(self.base_node) > 0, f'Houston, we have a problem!'
net_node_ref = self.__get_node_value(self.base_node[0])
diff --git a/tests/dev_tests/test_data/atom-extraction-devGraph-1.result.ttl b/tests/dev_tests/test_data/atom-extraction-devGraph-1.result.ttl
index f87267394544c131266b93c6325276f4427f6abf..67bb1f9a51f43f11e0beec1ad5699ef506d3f03d 100644
--- a/tests/dev_tests/test_data/atom-extraction-devGraph-1.result.ttl
+++ b/tests/dev_tests/test_data/atom-extraction-devGraph-1.result.ttl
@@ -440,9 +440,6 @@ net:Property_Direction a owl:Class ;
net:Restriction_Net a owl:Class ;
rdfs:subClassOf net:Net .
-net:Value_Net a owl:Class ;
- rdfs:subClassOf net:Net .
-
net:abstractionClass a owl:AnnotationProperty ;
rdfs:label "abstraction class" ;
rdfs:subPropertyOf net:objectValue .
@@ -708,6 +705,16 @@ net:unary_list a owl:Class ;
rdfs:label "unary-list" ;
rdfs:subClassOf net:list .
+net:value_SolarSystem_blankNode a net:Value_Net ;
+ net:hasNaming "SolarSystem" ;
+ net:hasStructure "SSC-01-01" ;
+ net:hasValueLabel "Solar System" .
+
+net:value_negative_blankNode a net:Value_Net ;
+ net:hasNaming "negative" ;
+ net:hasStructure "SSC-01-01" ;
+ net:hasValueLabel "negative" .
+
net:verbClass a owl:AnnotationProperty ;
rdfs:label "verb class" ;
rdfs:subPropertyOf net:objectValue .
@@ -1079,6 +1086,9 @@ net:Class_Net a owl:Class ;
net:Property_Net a owl:Class ;
rdfs:subClassOf net:Net .
+net:Value_Net a owl:Class ;
+ rdfs:subClassOf net:Net .
+
net:atomClass_system_s a net:Atom_Class_Net ;
:role_domain net:individual_SolarSystem_p ;
net:coverBaseNode :leaf_system_s ;
diff --git a/tests/dev_tests/test_rule_atomic_extractor.py b/tests/dev_tests/test_rule_atomic_extractor.py
index 46c56859f664ae1f0cda4badb6fbb807e2a43850..8fbd3fb5d2ea9c58a0f864900a308d5fdad9ee14 100644
--- a/tests/dev_tests/test_rule_atomic_extractor.py
+++ b/tests/dev_tests/test_rule_atomic_extractor.py
@@ -25,9 +25,11 @@ from context import tenet
from tenet.transduction.rdfterm_computer import __update_uri_with_prefix
from tenet.transduction import rdfterm_computer, prefix_handle
from tenet.transduction import net
-from tenet.scheme.amr_rule.transduction import atomic_class_extractor as rule_1
-from tenet.scheme.amr_rule.transduction import atomic_individual_extractor as rule_2
-from tenet.scheme.amr_rule.transduction import atomic_property_extractor as rule_3
+from tenet.scheme.amr_rule.transduction import atom_class_extractor as rule_1
+from tenet.scheme.amr_rule.transduction import atom_individual_extractor as rule_2
+from tenet.scheme.amr_rule.transduction import atom_property_extractor as rule_3
+from tenet.scheme.amr_rule.transduction import atom_value_extractor as rule_4
+from tenet.scheme.amr_rule.transduction import atom_phenomena_extractor as rule_5
from tenet.scheme import amr_rule
@@ -80,46 +82,55 @@ def add_triples_in_graph(graph, triple_list):
#==============================================================================
def test_search_pattern_1(graph):
- pattern_result_set = rule_1.__search_pattern(graph)
- print(f'\n ----- number of selection found: {len(pattern_result_set)}')
- for selection in pattern_result_set:
+ _, pattern_set = rule_1.__search_pattern(graph)
+ print(f'\n ----- number of selection found: {len(pattern_set)}')
+ for row in pattern_set:
result_str = f'>>> '
- result_str += f'{selection.leaf.n3(graph.namespace_manager)}'
- result_str += f' {selection.conceptName.n3(graph.namespace_manager)}'
+ result_str += f'{row.leaf.n3(graph.namespace_manager)}'
+ result_str += f' {row.conceptName.n3(graph.namespace_manager)}'
print(result_str)
- return pattern_result_set
+ return pattern_set
def test_search_pattern_2(graph):
- pattern_result_set = rule_2.__search_pattern(graph)
- print(f'\n ----- number of selection found: {len(pattern_result_set)}')
- for selection in pattern_result_set:
+ _, pattern_set = rule_2.__search_pattern(graph)
+ print(f'\n ----- number of selection found: {len(pattern_set)}')
+ for row in pattern_set:
result_str = f'>>> '
- result_str += f'{selection.baseLeaf.n3(graph.namespace_manager)}'
- result_str += f' {selection.conceptName.n3(graph.namespace_manager)}'
- result_str += f' {selection.valueLabel.n3(graph.namespace_manager)}'
- result_str += f' {selection.classNet.n3(graph.namespace_manager)}'
+ result_str += f'{row.baseLeaf.n3(graph.namespace_manager)}'
+ result_str += f' {row.valueLabel.n3(graph.namespace_manager)}'
+ result_str += f' {row.classNet.n3(graph.namespace_manager)}'
print(result_str)
- return pattern_result_set
+ return pattern_set
def test_search_pattern_3(graph):
- pattern_result_set = rule_3.__search_pattern(graph)
- print(f'\n ----- number of selection found: {len(pattern_result_set)}')
- for selection in pattern_result_set:
+ _, pattern_set = rule_3.__search_pattern(graph)
+ print(f'\n ----- number of selection found: {len(pattern_set)}')
+ for row in pattern_set:
result_str = f'>>> '
- result_str += f'{selection.baseLeaf.n3(graph.namespace_manager)}'
- result_str += f' {selection.predicateLabel.n3(graph.namespace_manager)}'
+ result_str += f'{row.baseLeaf.n3(graph.namespace_manager)}'
+ result_str += f' {row.predicateLabel.n3(graph.namespace_manager)}'
print(result_str)
- return pattern_result_set
+ return pattern_set
+
+
+def test_search_pattern_4(graph):
+ _, pattern_set = rule_4.__search_pattern(graph)
+ print(f'\n ----- number of selection found: {len(pattern_set)}')
+ for row in pattern_set:
+ result_str = f'>>> '
+ result_str += f'{row.valueLabel.n3(graph.namespace_manager)}'
+ print(result_str)
+ return pattern_set
def test_search_structure(graph):
result_set = rule_3.__search_structure(graph)
print(f'\n ----- number of selection found: {len(result_set)}')
- for selection in result_set:
+ for row in result_set:
result_str = f'>>> '
- result_str += f'{selection.sentenceId.n3(graph.namespace_manager)}'
+ result_str += f'{row.sentenceId.n3(graph.namespace_manager)}'
print(result_str)
return result_set
@@ -158,7 +169,7 @@ if __name__ == '__main__':
print('\n \n')
print('\n *** Unit Test ***')
- test_rule_application(graph, rule_1.extract_atomic_class)
+ test_rule_application(graph, rule_1.extract_atom_class)
print('\n \n')
print('\n ///////////////////// Extraction Rule 2')
@@ -170,7 +181,7 @@ if __name__ == '__main__':
print('\n \n')
print('\n *** Unit Test ***')
- test_rule_application(graph, rule_2.extract_atomic_individual)
+ test_rule_application(graph, rule_2.extract_atom_individual)
print('\n \n')
print('\n \n')
@@ -184,7 +195,20 @@ if __name__ == '__main__':
print('\n \n')
print('\n *** Unit Test ***')
- test_rule_application(graph, rule_3.extract_atomic_property)
+ test_rule_application(graph, rule_3.extract_atom_property)
+ print('\n \n')
+
+ print('\n ///////////////////// Extraction Rule 4')
+ print('\n *** Step Test ***')
+
+ print('\n -- Step 1: Search Pattern')
+ pattern_set = test_search_pattern_4(graph)
+ test_search_structure(graph)
+
+ print('\n \n')
+
+ print('\n *** Unit Test ***')
+ test_rule_application(graph, rule_4.extract_atom_value)
print('\n \n')
print('\n *** - ***')
\ No newline at end of file