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SPARQLML_examples.py
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SPARQLML_examples.py
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import pandas as pd
from Constants import *
import Constants
from KGNET import KGNET
from statistics import mean
if __name__ == '__main__':
#################################### NC ############################
# kgnet=KGNET(KG_endpointUrl='http://206.12.98.118:8890/sparql',KG_NamedGraph_IRI='http://www.aifb.uni-karlsruhe.de')
# model_info, transform_info, train_info = kgnet.train_GML(operatorType=Constants.GML_Operator_Types.NodeClassification, targetNodeType="aifb:Person",labelNodeType="aifb:ResearchGroup", GNNMethod=Constants.GNN_Methods.Graph_SAINT)
# kgnet = KGNET(KG_endpointUrl='http://206.12.98.118:8890/sparql', KG_NamedGraph_IRI='https://dblp2022.org',
# KG_Prefix='dblp2022')
# model_info, transform_info, train_info = kgnet.train_GML(operatorType=KGNET.GML_Operator_Types.NodeClassification,
# targetNodeType="dblp2022:Publication",
# labelNodeType="dblp2022:publishedIn_Obj",
# GNNMethod=KGNET.GNN_Methods.Graph_SAINT)
# model_info, transform_info, train_info = kgnet.train_GML(operatorType=Constants.GML_Operator_Types.NodeClassification, targetNodeType="aifb:Project",labelNodeType="aifb:Organization", GNNMethod=GNN_Methods.Graph_SAINT)
# model_info, transform_info, train_info= kgnet.train_GML(operatorType=Constants.GML_Operator_Types.NodeClassification,targetNodeType="aifb:Person",labelNodeType="aifb:ResearchGroup",GNNMethod=GNN_Methods.Graph_SAINT)
# # kgnet = KGNET(KG_endpointUrl='http://206.12.98.118:8890/sparql')
# # model_info, transform_info, train_info=kgnet.train_GML(operatorType=Constants.GML_Operator_Types.NodeClassification,targetNodeType="dblp:Publication",labelNodeType="dblp:venue",GNNMethod=GNN_Methods.Graph_SAINT)
# # model_info, transform_info, train_info = kgnet.train_GML(operatorType=Constants.GML_Operator_Types.NodeClassification, targetNodeType="dblp:Publication",labelNodeType="dblp:venue", GNNMethod=GNN_Methods.Graph_SAINT)
# print(model_info)
kgnet = KGNET(KG_endpointUrl='http://206.12.97.2:8890/sparql/', KG_NamedGraph_IRI='https://yago-knowledge.org',KG_Prefix='yago')
# kgnet = KGNET(KG_endpointUrl='http://206.12.98.118:8890/sparql', KG_NamedGraph_IRI='http://wikikg-v2',KG_Prefix='WikiKG2015_v2')
# kgnet = KGNET(KG_endpointUrl='http://206.12.97.2:8890/sparql', KG_NamedGraph_IRI='https://yago-knowledge.org', KG_Prefix='yago')
# types_df = kgnet.getKGNodeEdgeTypes_V2(write_to_file=True, prefix='yago')
# types_df = types_df[(~types_df["object"].str.endswith("_Obj")) | (types_df["object"].str.endswith("publishedIn_Obj"))]
# types_df
# TargetEdge = "http://www.wikidata.org/entity/P166" # WikidataKG award received
# label_type="science_or_engineering_award"
# TargetEdge = "http://www.wikidata.org/entity/P101" # WikidataKG work field# area_of_mathematics
# label_type = "area_of_mathematics"
# TargetEdge = "http://www.wikidata.org/entity/P27" # citizenship
# label_type="country"
# TargetEdge = "http://www.wikidata.org/entity/P106" # profession
# label_type = "occupation"
#TargetEdge = "http://www.wikidata.org/entity/P108" # Employeer
#"http://www.wikidata.org/entity/Q3571662" Yan Lucn
TargetEdge = "dblp:publishedIn" # profession
targetNodeType = "dblp:Publication"
MinInstancesPerLabel = 2260
TargetEdge = "dblp:bibtexType" # profession
targetNodeType = "dblp:Publication"
MinInstancesPerLabel = 1000
# TargetEdge = "https://dblp.org/Affaliation_Country"
# targetNodeType = "dblp:Person"
# MinInstancesPerLabel = 115
TargetEdge = "yago:alumniOf" # profession
targetNodeType = "yago:Person"
MinInstancesPerLabel = 100
TargetEdge = "yago:nationality" # profession
targetNodeType = "yago:Person"
MinInstancesPerLabel = 10
for epoch in range(5,31,5):
for e_size in range(64, 128, 32):
model_info, transform_info, train_info = kgnet.train_GML(operatorType=Constants.GML_Operator_Types.NodeClassification, targetNodeType=targetNodeType,labelNodeType=None,targetEdge=TargetEdge, GNNMethod=GNN_Methods.Graph_SAINT,TOSG_Pattern=TOSG_Patterns.d1h1,epochs=epoch,emb_size=e_size,MinInstancesPerLabel=MinInstancesPerLabel)
#################################### LP ######
# ######################
# kgnet = KGNET(KG_endpointUrl='http://206.12.98.118:8890/sparql', KG_NamedGraph_IRI='http://www.aifb.uni-karlsruhe.de',KG_Prefix="aifb")
# model_info, transform_info, train_info = kgnet.train_GML(
# operatorType=Constants.GML_Operator_Types.LinkPrediction, targetEdge="http://swrc.ontoware.org/ontology#publication", GNNMethod=GNN_Methods.RGCN)
# kgnet = KGNET(KG_endpointUrl='http://206.12.98.118:8890/sparql', KG_NamedGraph_IRI='https://dblp2022.org',KG_Prefix='dblp2022')
# kgnet = KGNET(KG_endpointUrl='http://206.12.100.35:5820/kgnet_kgs/query',KGMeta_endpointUrl='http://206.12.100.35:5820/kgnet_kgs/query', KG_NamedGraph_IRI='https://dblp2022.org',KG_Prefix='dblp2022',RDFEngine=Constants.RDFEngine.stardog)
# types_df = kgnet.getKGNodeEdgeTypes(write_to_file=True, prefix='dblp2022')
# task_id,mode_id,model_info_dict = kgnet.train_GML(operatorType=Constants.GML_Operator_Types.NodeClassification, targetNodeType="dblp2022:Publication",labelNodeType="dblp2022:publishedIn_Obj", GNNMethod=GNN_Methods.Graph_SAINT)
# task_id='tid-0000025'
# df = kgnet.KGMeta_Governer.getGMLTaskModelsBasicInfoByID(task_id)
# print(model_info_dict)
#model_info, transform_info, train_info = kgnet.train_GML(operatorType=Constants.GML_Operator_Types.LinkPrediction,targetEdge="http://swrc.ontoware.org/ontology#author",GNNMethod=GNN_Methods.MorsE)
#kgnet = KGNET(KG_endpointUrl='http://206.12.98.118:8890/sparql', KG_NamedGraph_IRI='https://dblp2022.org',KG_Prefix='dblp2022')
kgnet = KGNET(KG_endpointUrl='http://206.12.98.118:8890/sparql', KG_NamedGraph_IRI='http://wikikg-v2',KG_Prefix='WikiKG2015_v2')
# KGNET.inference_path = KGNET.KGNET_Config.datasets_output_path + 'Inference/'
# KGNET.KGNET_Config.trained_model_path = KGNET.KGNET_Config.datasets_output_path + 'trained_models/'
#
# KGNET.KGNET_Config.GML_API_URL = "http://206.12.102.12:64647/"
# KGNET.KGNET_Config.fileStorageType = FileStorageType.remoteFileStore
# #########remoteFileStore######
# KGNET.KGNET_Config.GML_ModelManager_URL = "http://206.12.102.12"
# KGNET.KGNET_Config.GML_ModelManager_PORT = "64648"
# KGNET.KGNET_Config.KGMeta_IRI = "http://kgnet/"
# KGNET.KGNET_Config.KGMeta_endpoint_url = "http://206.12.98.118:8890/sparql/"
#
# TargetEdge = "https://dblp.org/rdf/schema#authoredBy"
# model_info, transform_info, train_info = kgnet.train_GML(operatorType=KGNET.GML_Operator_Types.LinkPrediction,
# targetEdge=TargetEdge,
# GNNMethod=KGNET.GNN_Methods.RGCN)
# TargetEdge = "http://www.wikidata.org/entity/P166" # WikidataKG award received
# TargetEdge= "http://www.wikidata.org/entity/P101" # WikidataKG field of work
# model_info, transform_info, train_info = kgnet.train_GML(operatorType=KGNET.GML_Operator_Types.LinkPrediction,
# targetEdge=TargetEdge,
# GNNMethod=KGNET.GNN_Methods.RGCN)
################################## Single Task SPARQLML##########################
inference_query_NC = """
prefix aifb:<http://swrc.ontoware.org/ontology#>
prefix kgnet:<http://kgnet/>
select ?person ?aff
from <http://www.aifb.uni-karlsruhe.de>
where
{
?person a aifb:Person.
?person ?NodeClassifier ?aff.
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> aifb:Person.
?NodeClassifier <kgnet:labelNode> aifb:ResearchGroup.
}
limit 100
"""
inference_query_NC2="""
prefix dblp2022:<https://dblp.org/rdf/schema#>
prefix kgnet:<http://kgnet/>
select ?Publication ?Title ?Org_Venue ?Pred_Venue
from <https://dblp2022.org>
where
{
?Publication a dblp2022:Publication .
?Publication ?NodeClassifier ?Pred_Venue .
?Publication dblp2022:publishedIn ?Org_Venue .
?Publication dblp2022:title ?Title .
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> dblp2022:Publication.
?NodeClassifier <kgnet:labelNode> dblp2022:publishedIn_Obj.
}
limit 10
"""
inference_query_LP = """
prefix dblp2022:<https://dblp.org/rdf/schema#>
prefix kgnet:<https://kgnet/>
select ?publication ?author
from <https://dblp2022.org>
where {
?publication a dblp2022:Publication.
?publication ?LinkPredictor ?author.
?LinkPredictor a <kgnet:types/LinkPredictor>.
?LinkPredictor <kgnet:targetEdge> "https://dblp.org/rdf/schema#authoredBy" .
?LinkPredictor <kgnet:GNNMethod> "MorsE" .
?LinkPredictor <kgnet:topK> 3 .
}
limit 300
offset 0
"""
inference_query_wikidata_workField_NC = """
prefix wiki:<http://www.wikidata.org/entity/>
prefix kgnet:<http://kgnet/>
select ?human ?work
from <http://wikikg-v2>
where
{
?human wiki:P101 ?w.
?human a "human".
?w a "area_of_mathematics".
?human ?NodeClassifier ?work.
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> "human".
?NodeClassifier <kgnet:labelNode> "area_of_mathematics".
?NodeClassifier <kgnet:targetEdge> "http://www.wikidata.org/entity/P101".
}
limit 100
"""
inference_query_wikidata_award_NC = """
prefix wiki:<http://www.wikidata.org/entity/>
prefix kgnet:<http://kgnet/>
select ?human ?award
from <http://wikikg-v2>
where
{
?human wiki:P166 ?w.
?human a "human".
?w a "science_or_engineering_award".
?human ?NodeClassifier ?award.
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> "human".
?NodeClassifier <kgnet:labelNode> "science_or_engineering_award".
?NodeClassifier <kgnet:targetEdge> "http://www.wikidata.org/entity/P166".
}
limit 100
"""
nested_Query = """ select (count(*) as ?s)
from <http://wikikg-v2>
where
{
?s ?p ?o.
{
select distinct ?s
where
{
?s <http://www.wikidata.org/entity/P69> ?o.
?s a "human".
?o <http://schema.org/description> ?ol.
}
}
}
"""
################################## Multi-Task SPARQLML##########################
###################### Wikikdata Multi-Task SPARQLML##################
inference_query_wikidata_award_workField_NC = """
prefix wiki:<http://www.wikidata.org/entity/>
prefix kgnet:<http://kgnet/>
select ?human ?univ ?pred_award ?pred_work
from <http://wikikg-v2>
where
{
?human wiki:P166 ?award.
?human a "human".
?human wiki:P69 ?univ .
?award a "science_or_engineering_award".
?human ?NodeClassifier ?pred_award.
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> "human".
?NodeClassifier <kgnet:labelNode> "science_or_engineering_award".
?NodeClassifier <kgnet:targetEdge> "http://www.wikidata.org/entity/P166".
?human wiki:P101 ?work.
?work a "area_of_mathematics".
?human ?NodeClassifier2 ?pred_work.
?NodeClassifier2 a <kgnet:types/NodeClassifier>.
?NodeClassifier2 <kgnet:targetNode> "human".
?NodeClassifier2 <kgnet:labelNode> "area_of_mathematics".
?NodeClassifier2 <kgnet:targetEdge> "http://www.wikidata.org/entity/P101".
}
limit 100
"""
inference_query_wikidata_award_workField_univ_NC = """
prefix wiki:<http://www.wikidata.org/entity/>
prefix kgnet:<http://kgnet/>
select ?human ?univ_label ?pred_awardLabel ?pred_workFieldLabel
from <http://wikikg-v2>
where
{
?pred_award_ent <http://www.w3.org/2000/01/rdf-schema#label> ?pred_awardLabel.
?pred_work_ent <http://www.w3.org/2000/01/rdf-schema#label> ?pred_workFieldLabel.
filter(?pred_awardLabel='Royal Medal').
filter(?pred_workFieldLabel='number theory').
{
select distinct ?human ?univ_label (IRI(?pred_award) as ?pred_award_ent) (IRI(?pred_work) as ?pred_work_ent)
where
{
?human wiki:P166 ?award.
?human a "human".
?human wiki:P69 ?univ .
?univ <http://www.w3.org/2000/01/rdf-schema#label> ?univ_label.
#optional {?univ <http://www.w3.org/2000/01/rdf-schema#label> ?univ_label. }
?award a "science_or_engineering_award".
?human ?NodeClassifier ?pred_award.
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> "human".
?NodeClassifier <kgnet:labelNode> "science_or_engineering_award".
?NodeClassifier <kgnet:targetEdge> "http://www.wikidata.org/entity/P166".
?human wiki:P101 ?work.
?work a "area_of_mathematics".
?human ?NodeClassifier2 ?pred_work.
?NodeClassifier2 a <kgnet:types/NodeClassifier>.
?NodeClassifier2 <kgnet:targetNode> "human".
?NodeClassifier2 <kgnet:labelNode> "area_of_mathematics".
?NodeClassifier2 <kgnet:targetEdge> "http://www.wikidata.org/entity/P101".
}
#limit 100
}
}
"""
inference_query_wikidata_award_workField_univ_NC_v2 = """
prefix wiki:<http://www.wikidata.org/entity/>
prefix kgnet:<http://kgnet/>
select distinct ?human ?pred_awardLabel ?pred_workFieldLabel
from <http://wikikg-v2>
where
{
?human a "human".
?human wiki:P69 ?univ .
optional {?univ <http://www.w3.org/2000/01/rdf-schema#label> ?univ_label }.
?human wiki:P166 ?award.
?award a "science_or_engineering_award".
?human wiki:P101 ?work.
?work a "area_of_mathematics".
?human ?NodeClassifier ?pred_award.
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> "human".
?NodeClassifier <kgnet:labelNode> "science_or_engineering_award".
?NodeClassifier <kgnet:targetEdge> "http://www.wikidata.org/entity/P166".
?human ?NodeClassifier2 ?pred_work.
?NodeClassifier2 a <kgnet:types/NodeClassifier>.
?NodeClassifier2 <kgnet:targetNode> "human".
?NodeClassifier2 <kgnet:labelNode> "area_of_mathematics".
?NodeClassifier2 <kgnet:targetEdge> "http://www.wikidata.org/entity/P101".
?pred_award <http://www.w3.org/2000/01/rdf-schema#label> ?pred_awardLabel.
?pred_work <http://www.w3.org/2000/01/rdf-schema#label> ?pred_workFieldLabel.
filter(?pred_awardLabel='Royal Medal').
#filter(?pred_awardLabel='Fields Medal').
filter(?pred_workFieldLabel='number theory').
}
#limit 100
"""
inference_query_wikidata_award_univ_NC_v2 = """
prefix wiki:<http://www.wikidata.org/entity/>
prefix kgnet:<http://kgnet/>
select distinct ?human ?pred_awardLabel
from <http://wikikg-v2>
where
{
?human a "human".
?human wiki:P69 ?univ .
?univ <http://www.w3.org/2000/01/rdf-schema#label> ?univ_label.
?human wiki:P166 ?award.
?award a "science_or_engineering_award".
?human ?NodeClassifier ?pred_award.
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> "human".
?NodeClassifier <kgnet:labelNode> "science_or_engineering_award".
?NodeClassifier <kgnet:targetEdge> "http://www.wikidata.org/entity/P166".
?human wiki:P101 ?work.
?work a "area_of_mathematics".
?pred_award <http://www.w3.org/2000/01/rdf-schema#label> ?pred_awardLabel.
filter(?pred_awardLabel='Royal Medal').
}
#limit 100
"""
inference_query_wikidata_workField_univ_NC_v2 = """
prefix wiki:<http://www.wikidata.org/entity/>
prefix kgnet:<http://kgnet/>
select distinct ?human ?pred_workFieldLabel
from <http://wikikg-v2>
where
{
?human a "human".
?human wiki:P69 ?univ .
?univ <http://www.w3.org/2000/01/rdf-schema#label> ?univ_label.
?human wiki:P166 ?award.
?award a "science_or_engineering_award".
?human wiki:P101 ?work.
?work a "area_of_mathematics".
?human ?NodeClassifier2 ?pred_work.
?NodeClassifier2 a <kgnet:types/NodeClassifier>.
?NodeClassifier2 <kgnet:targetNode> "human".
?NodeClassifier2 <kgnet:labelNode> "area_of_mathematics".
?NodeClassifier2 <kgnet:targetEdge> "http://www.wikidata.org/entity/P101".
?pred_work <http://www.w3.org/2000/01/rdf-schema#label> ?pred_workFieldLabel.
filter(?pred_workFieldLabel='number theory').
}
#limit 100
"""
###################### DBLP Multi-Task SPARQLML##################
inference_MQuery_NC = """
prefix dblp2022:<https://dblp.org/rdf/schema#>
prefix kgnet:<http://kgnet/>
select ?Publication ?Title ?Org_Venue ?Pred_Venue
from <https://dblp2022.org>
where
{
?Publication a dblp2022:Publication .
?Publication dblp2022:title ?Title .
?Publication ?authored_by ?Author .
?Publication dblp2022:publishedIn ?Org_Venue .
?Auhor ?aff ?Org_Aff_Country .
?Publication ?NodeClassifier ?Pred_Venue .
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> dblp2022:Publication.
?NodeClassifier <kgnet:labelNode> dblp2022:publishedIn_Obj.
?Auhor ?NodeClassifier2 ?Pred_Aff_Country .
?NodeClassifier2 a <kgnet:types/NodeClassifier>.
?NodeClassifier2 <kgnet:targetNode> dblp2022:Author.
?NodeClassifier2 <kgnet:labelNode> dblp2022:Country_Obj.
}
limit 10
"""
inference_MQuery_dblp2022_NC_LP = """
prefix dblp2022:<https://dblp.org/rdf/schema#>
prefix kgnet:<http://kgnet/>
select ?Publication ?Title ?Org_Venue ?Pred_Venue ?Org_author ?Pred_author
from <https://dblp2022.org>
where
{
?Publication a dblp2022:Publication .
?Publication dblp2022:title ?Title .
?Publication dblp2022:publishedIn ?Org_Venue .
?Publication dblp2022:authoredBy ?Org_author .
?Publication ?NodeClassifier ?Pred_Venue .
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> dblp2022:Publication.
?NodeClassifier <kgnet:labelNode> dblp2022:publishedIn_Obj.
?Publication ?LinkPredictor ?Pred_author.
?LinkPredictor a <kgnet:types/LinkPredictor>.
?LinkPredictor <kgnet:targetEdge> "https://dblp.org/rdf/schema#authoredBy" .
?LinkPredictor <kgnet:GNNMethod> "MorsE" .
?LinkPredictor <kgnet:topK> 3 .
}
limit 10
"""
inference_MQuery_dblp_NC_venue_aff="""
prefix dblp:<https://dblp.org/rdf/schema#>
select distinct ?Publication ?venue ?country
from <http://dblp.org>
where
{
?Publication a dblp:Publication .
?Publication dblp:publishedIn ?venue.
?Publication dblp:yearOfPublication ?year .
?Publication dblp:authoredBy ?Author.
?Publication dblp:bibtexType ?ptype.
?Author a dblp:Person.
?Author <https://dblp.org/Affaliation_Country> ?country.
?Publication ?NodeClassifier ?Pred_Venue .
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> dblp:Publication.
?NodeClassifier <kgnet:targetEdge> dblp:publishedIn.
?Publication ?NodeClassifier2 ?pred_ptype .
?NodeClassifier2 a <kgnet:types/NodeClassifier>.
?NodeClassifier2 <kgnet:targetNode> dblp:Publication.
?NodeClassifier2 <kgnet:targetEdge> dblp:bibtexType.
?Author ?NodeClassifier3 ?Pred_aff_country .
?NodeClassifier3 a <kgnet:types/NodeClassifier>.
?NodeClassifier3 <kgnet:targetNode> dblp:Person.
?NodeClassifier3 <kgnet:targetEdge> "https://dblp.org/Affaliation_Country".
filter(xsd:integer(?year)>=2020).
# filter(?country not in ("germany","china",'usa')).
# filter(?venue not in ("AAAI","VLDB","ACM",'CoRR')).
# filter(?venue in ( "Proc. VLDB Endow.","Symmetry","Entropy","IEEE Trans. Knowl. Data Eng.","Autom.","Bioinform.")).
# filter(?country not in ("germany","china")).
# filter(?venue not in ("AAAI","VLDB","ACM")).
# filter (?ptype in (<http://purl.org/net/nknouf/ns/bibtex#Article>)).
filter(?country in ("germany","china","usa")).
filter(?venue in ("IEEE Access","Remote. Sens.")).
#filter (?ptype in (<http://purl.org/net/nknouf/ns/bibtex#Inproceedings>)).
filter(str(?Pred_aff_country) in ("germany","china","usa")).
filter(?Pred_Venue in ("IEEE Access","Remote. Sens.")).
filter (?pred_ptype not in (<http://purl.org/net/nknouf/ns/bibtex#Inproceedings>)).
# filter(str(?country) in ("germany","china")).
# filter(?venue in ("IEEE Trans. Ind. Informatics","Remote. Sens.")).
# filter(str(?Pred_aff_country) in ("germany","china")).
# filter(?Pred_Venue in ("IEEE Trans. Ind. Informatics","Remote. Sens.")).
}
"""
inference_MQuery_dblp_NC_venue_aff_v2 = """
prefix dblp:<https://dblp.org/rdf/schema#>
select distinct ?venue count(*)
from <http://dblp.org>
where
{
?Publication a dblp:Publication .
?Publication <https://dblp.org/rdf/schema#publishedIn> ?venue.
?Publication <https://dblp.org/rdf/schema#authoredBy> ?Author.
?Author a <https://dblp.org/rdf/schema#Person>.
?Author <https://dblp.org/Affaliation_Country> ?aff_country.
?Publication ?NodeClassifier ?Pred_Venue .
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> dblp:Publication.
?NodeClassifier <kgnet:targetEdge> dblp:publishedIn.
?Author ?NodeClassifier2 ?Pred_aff_country .
?NodeClassifier2 a <kgnet:types/NodeClassifier>.
?NodeClassifier2 <kgnet:targetNode> dblp:Person.
?NodeClassifier2 <kgnet:targetEdge> "https://dblp.org/Affaliation_Country".
#filter (?Pred_aff_country in ("china","usa","germany"))
#filter (?Pred_Venue in ("VLDB","ACM"))
filter(str(?Pred_aff_country) in ("germany","china")).
filter(?Pred_Venue in ("IEEE Trans. Ind. Informatics","Remote. Sens.")).
}
group by ?venue
having (count(*)>1)
order by DESC(count(*))
"""
#############################
inference_query_wikidata_Citizenship_Profession_univ_NC_v2 = """
prefix wiki:<http://www.wikidata.org/entity/>
prefix kgnet:<http://kgnet/>
select distinct ?human ?pred_nationality_label ?pred_profession_label
from <http://wikikg-v2>
where
{
?human a "human".
?human wiki:P27 ?nationality.
?nationality a "country".
?human wiki:P106 ?profession.
?profession a "occupation".
?human ?NodeClassifier ?pred_nationality.
?NodeClassifier a <kgnet:types/NodeClassifier>.
?NodeClassifier <kgnet:targetNode> "human".
?NodeClassifier <kgnet:labelNode> "country".
?NodeClassifier <kgnet:labelValue> "Nederland".
?NodeClassifier <kgnet:targetEdge> "http://www.wikidata.org/entity/P27".
?human ?NodeClassifier2 ?pred_profession.
?NodeClassifier2 a <kgnet:types/NodeClassifier>.
?NodeClassifier2 <kgnet:targetNode> "human".
?NodeClassifier2 <kgnet:labelNode> "occupation".
?NodeClassifier2 <kgnet:labelValue> "baseball player".
?NodeClassifier2 <kgnet:targetEdge> "http://www.wikidata.org/entity/P106".
?pred_nationality <http://www.w3.org/2004/02/skos/core#altLabel> ?pred_nationality_label.
#?pred_profession <http://www.w3.org/2004/02/skos/core#altLabel> ?pred_profession_label.
?pred_profession <http://www.w3.org/2000/01/rdf-schema#label> ?pred_profession_label.
filter(?pred_nationality_label='Nederland').
filter(?pred_profession_label='baseball player').
}
#limit 100
"""
# kgnet=KGNET(KG_endpointUrl='http://206.12.98.118:8890/sparql',KG_NamedGraph_IRI='https://dblp2022.org',KGMeta_endpointUrl="http://206.12.98.118:8890/sparql",RDFEngine=RDFEngine.OpenlinkVirtuoso)
# kgnet = KGNET(KG_endpointUrl="http://206.12.100.35:5820/kgnet_kgs/query",KGMeta_endpointUrl="http://206.12.100.35:5820/kgnet_kgs/query", KG_NamedGraph_IRI='https://dblp2022.org',RDFEngine=RDFEngine.stardog)
# resDF, MetaQueries = kgnet.executeSPARQLMLInferenceQuery(inference_query_wikidata_workField_NC)
# resDF, MetaQueries = kgnet.executeSPARQLMLInferenceQuery(inference_query_wikidata_award_NC)
# resDF, MetaQueries = kgnet.executeSPARQLMLInferenceQuery(inference_query_wikidata_award_workField_univ_NC)
# resDF, MetaQueries = kgnet.executeSPARQLMLInferenceQuery(inference_query_wikidata_award_univ_NC_v2)
# resDF, MetaQueries = kgnet.executeSPARQLMLInferenceQuery(inference_query_wikidata_workField_univ_NC_v2)
# resDF, MetaQueries = kgnet.executeSPARQLMLInferenceQuery(inference_query_wikidata_Citizenship_Profession_univ_NC_v2)
# field_medal_number_theory_Kg_humans=['http://www.wikidata.org/entity/Q1398727','http://www.wikidata.org/entity/Q211041','http://www.wikidata.org/entity/Q212063','http://www.wikidata.org/entity/Q220402','http://www.wikidata.org/entity/Q295981','http://www.wikidata.org/entity/Q310769','http://www.wikidata.org/entity/Q333538','http://www.wikidata.org/entity/Q333968','http://www.wikidata.org/entity/Q334045','http://www.wikidata.org/entity/Q369561','http://www.wikidata.org/entity/Q77137']
# royal_medal_number_theory_Kg_humans=['http://www.wikidata.org/entity/Q184337', 'http://www.wikidata.org/entity/Q184433','http://www.wikidata.org/entity/Q295981', 'http://www.wikidata.org/entity/Q310781','http://www.wikidata.org/entity/Q353426']
# nethierland_athelete_Kg_humans = 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# dblp_AAAI_germany = ["https://dblp.org/rec/conf/aaai/0002K21", "https://dblp.org/rec/conf/aaai/TomaniB21","https://dblp.org/rec/conf/aaai/KupcsikSKTWSB21", "https://dblp.org/rec/conf/aaai/PhuocEL21","https://dblp.org/rec/conf/aaai/LuoQXCZDYZWCHRL21", "https://dblp.org/rec/conf/aaai/MohrBH21","https://dblp.org/rec/conf/aaai/PanthaplackelAB21","https://dblp.org/rec/conf/aaai/BentertBG0N21", "https://dblp.org/rec/conf/aaai/Baier0M21","https://dblp.org/rec/conf/aaai/Bilo0LLM21", "https://dblp.org/rec/conf/aaai/0002M021","https://dblp.org/rec/conf/aaai/Behnke021", "https://dblp.org/rec/conf/aaai/WilhelmK21","https://dblp.org/rec/conf/aaai/BodirskyK21", "https://dblp.org/rec/conf/aaai/GilT21","https://dblp.org/rec/conf/aaai/FrikhaKKT21", "https://dblp.org/rec/conf/aaai/Dvorak0W21","https://dblp.org/rec/conf/aaai/KotnisLN21", "https://dblp.org/rec/conf/aaai/DalyMAN21","https://dblp.org/rec/conf/aaai/FichteHM21", "https://dblp.org/rec/conf/aaai/Rothe21","https://dblp.org/rec/conf/aaai/0001W21a", "https://dblp.org/rec/conf/aaai/UnalAP21","https://dblp.org/rec/conf/aaai/0001BW21", "https://dblp.org/rec/conf/aaai/BrosowskyKD021","https://dblp.org/rec/conf/aaai/FickertGF0MR21","https://dblp.org/rec/conf/aaai/BesserveSJS21", "https://dblp.org/rec/conf/aaai/HedderichZK21","https://dblp.org/rec/conf/aaai/LuoZCQDZWCHRL21","https://dblp.org/rec/conf/aaai/LawrenceSN21", "https://dblp.org/rec/conf/aaai/LedentMLK21","https://dblp.org/rec/conf/aaai/HollerB21", "https://dblp.org/rec/conf/aaai/MianMV21","https://dblp.org/rec/conf/aaai/SharifzadehBT21", "https://dblp.org/rec/conf/aaai/Potyka21","https://dblp.org/rec/conf/aaai/MathewSYBG021", "https://dblp.org/rec/conf/aaai/PhanBGSRL21","https://dblp.org/rec/conf/aaai/ShaoSSSK21", "https://dblp.org/rec/conf/aaai/WienobstBL21","https://dblp.org/rec/conf/aaai/0001FBBQZDSM021", "https://dblp.org/rec/conf/aaai/0001CDM21","https://dblp.org/rec/conf/aaai/ArtaleJMOW21", "https://dblp.org/rec/conf/aaai/00010Q0LK21","https://dblp.org/rec/conf/aaai/WuLLK21", "https://dblp.org/rec/conf/aaai/HeegerHMMNS21","https://dblp.org/rec/conf/aaai/DennisB0021", "https://dblp.org/rec/conf/aaai/KruseDKS21","https://dblp.org/rec/conf/aaai/LienenH21", "https://dblp.org/rec/conf/aaai/TorralbaSKS021","https://dblp.org/rec/conf/aaai/NeiderGGT0021", "https://dblp.org/rec/conf/aaai/TanNDNB21","https://dblp.org/rec/conf/aaai/NayyeriVA021", "https://dblp.org/rec/conf/aaai/Torralba21","https://dblp.org/rec/conf/aaai/ZhouLHLZK21"]
# dblp_AAAI_ACM_VLDB_germany_china_2021=pd.read_csv("SPARQLML_HardAnswerResults/dblp_AAAI_ACM_VLDB_germany_china_2021.csv",header=None,sep=",")[0].tolist()
# dblp_Informatics_RemoteSens_germany_china_2022 = pd.read_csv("SPARQLML_HardAnswerResults/dblp_Informatics_RemoteSens_germany_china_2022.csv", header=None, sep=",")[0].tolist()
# dblp_notin_AAAI_ACM_VLDB_CoRR_germany_china_usa_2022 =pd.read_csv("SPARQLML_HardAnswerResults/dblp_notin_AAAI_ACM_VLDB_CoRR_germany_china_usa_2022.csv", header=None, sep=",")[0].tolist()
# dblp_notin_AAAI_ACM_VLDB_CoRR_germany_china_usa_2021 = pd.read_csv("SPARQLML_HardAnswerResults/dblp_notin_AAAI_ACM_VLDB_CoRR_germany_china_usa_2021.csv", header=None,sep=",")[0].tolist()
# dblp_AAAI_VLDB_germany_china_usa_Inproccesding_2021 =pd.read_csv("SPARQLML_HardAnswerResults/dblp_AAAI_VLDB_germany_china_usa_Inproccesding_2021.csv", header=None,sep=",")[0].tolist()
# dblp_AAAI_VLDB_germany_china_usa_Inproccesding_gte2019 = pd.read_csv("SPARQLML_HardAnswerResults/dblp_AAAI_VLDB_germany_china_usa_Inproccesding_gte2019.csv", header=None, sep=",")[0].tolist()
# dblp_CVPR_ICASSP_IGARSS_ICML_NeurIPS_germany_china_usa_Inproccesding_gte2020=pd.read_csv("SPARQLML_HardAnswerResults/dblp_CVPR_ICASSP_IGARSS_ICML_NeurIPS_germany_china_usa_Inproccesding_gte2020.csv", header=None, sep=",")[0].tolist()
# dblp_in_VLDB_Symmetry_Entropy_IEEEData_Eng_Autom_Bioinform_notin_germany_in_Article_gte2020 = pd.read_csv("SPARQLML_HardAnswerResults/dblp_in_VLDB_Symmetry_Entropy_IEEEData-Eng_Autom_Bioinform_notin_germany_in_Article_gte2020.csv",header=None, sep=",")[0].tolist()
True_Targets_Path="dblp_in_IEEEAccess_RemoteSens_in_germany_china_usa_notin_Inproccesding_gte_2020.csv"
True_Targets_df = pd.read_csv(f"SPARQLML_HardAnswerResults/{True_Targets_Path}",header=None, sep=",")[0].tolist()
Real_target_node=list(set([str(x).replace("\"","").strip() for x in True_Targets_df]))
# Real_target_node = nethierland_baseball_Kg_humans
# target_node="human"
target_node = "Publication"
import itertools
piplines=list(itertools.permutations(range(0,3)))
print(f"True_Targets_Path={True_Targets_Path}")
for pipline_no,pipline in enumerate(piplines):
print(f"################## Pipline({pipline})#######################")
exec_time = []
true_prad_lst, not_pred_lst, false_pred_lst = [], [], []
for run in range(0,3):
print(f"###### Run({run})#####")
# inference_query_wikidata_Citizenship_Profession_univ_NC_v2
resDF, MetaQueries,query_time_sec = kgnet.executeSPARQLMLInferenceQuery(inference_MQuery_dblp_NC_venue_aff,pipline=pipline_no)
# resDF, MetaQueries, query_time_sec = kgnet.executeSPARQLMLInferenceQuery(inference_query_wikidata_Citizenship_Profession_univ_NC_v2,in_pipline=True)
exec_time.append(query_time_sec)
# resDF,MetaQueries=kgnet.executeSPARQLMLInferenceQuery(inference_MQuery_dblp2022_NC_LP)
#resDF, MetaQueries = kgnet.executeSPARQLMLInferenceQuery(inference_query_wikidata_award_workField_NC)
# resDF, MetaQueries = kgnet.executeSPARQLMLInferenceQuery(nested_Query)
print("query_time_sec=",query_time_sec)
print(resDF)
pred_target_set=set([str(elem).replace("\"","").strip() for elem in set(resDF[target_node].tolist())])
true_prad_lst.append(len(pred_target_set.intersection(set(Real_target_node)))/len(Real_target_node))
print(f"True Predictions Ratio:{true_prad_lst[-1]}")
not_pred_lst.append(len(set(Real_target_node)-pred_target_set) / len(Real_target_node))
print(f"Not Predicted Ratio:{not_pred_lst[-1]}")
false_pred_lst.append(len(pred_target_set-set(Real_target_node)) / len(Real_target_node))
print(f"False Predictions Ratio:{false_pred_lst[-1]}")
print(MetaQueries)
# print("candidateSparqlQuery=",MetaQueries['candidateSparqlQuery'])
print(f"Pipline=({pipline})")
print(f"avg_time={mean(exec_time)} Sec.")
print(f"avg_true_prads={mean(true_prad_lst)*100}% .")
print(f"avg_not_preds={mean(not_pred_lst)*100}% .")
print(f"avg_false_preds={mean(false_pred_lst)*100}% .")
#############################################3