extract_TeachStud.py

# Code to extract Teacher and Student associated with each person
import rdflib
import os 
from rdflib.namespace import RDF, RDFS, SKOS, OWL, Namespace, NamespaceManager, XSD, URIRef
import csv
import pyewts
import sys

BDR = Namespace("http://purl.bdrc.io/resource/")
BDO = Namespace("http://purl.bdrc.io/ontology/core/")
BDA = Namespace("http://purl.bdrc.io/admindata/")
BDG = Namespace("http://purl.bdrc.io/graph/")
ADM = Namespace("http://purl.bdrc.io/ontology/admin/")
WD = Namespace("http://www.wikidata.org/entity/")
WDT = Namespace("http://www.wikidata.org/prop/direct/")
DILA = Namespace("http://purl.dila.edu.tw/resource/")
VIAF = Namespace("http://viaf.org/viaf/")

NSM = NamespaceManager(rdflib.Graph())
NSM.bind("bdr", BDR)
NSM.bind("bdg", BDG)
NSM.bind("bdo", BDO)
NSM.bind("bda", BDA)
NSM.bind("adm", ADM)
NSM.bind("skos", SKOS)
NSM.bind("rdfs", RDFS)
NSM.bind("wd", WD)
NSM.bind("owl", OWL)
NSM.bind("wdt", WDT)
NSM.bind("dila", DILA)
NSM.bind("viaf", VIAF)

# see https://github.com/RDFLib/rdflib/issues/806
if rdflib.__version__ == '4.2.2':
    x = rdflib.term._toPythonMapping.pop(rdflib.XSD['gYear'])

converter = pyewts.pyewts()

# Function to extract values for teachers and students
def extract(g, id):
    values = {}
    typeD = ""
    
    # ID's Teachers
    for _, _, teID in g.triples((BDR[id], BDO.personStudentOf, None)): 
        # Gets teachers of a person
        typeD = "teachers"
        _, _, teachID = NSM.compute_qname_strict(teID)
        if typeD not in values:
            values[typeD] = []
        values[typeD].append(teachID)
    
    # ID's Students
    for _, _, stID in g.triples((BDR[id], BDO.personTeacherOf, None)):
        # Gets students of a person
        typeD = "students"
        _, _, studID = NSM.compute_qname_strict(stID)
        if typeD not in values:
            values[typeD] = []
        values[typeD].append(studID)
    
    return values

# Creates list with the extracted data
def createList(personID, vals, COUNTPROP):
    row = []
    row.append(personID)

    for tp, nbcols in COUNTPROP.items():
        if tp not in vals:
            continue
        if nbcols < len(vals[tp]):
            print("!!Error!! There should be at least %i columns for %s aliases" % (len(vals[tp]), tp))
            print(personID)
            continue
        for i in range(nbcols):
            if i < len(vals[tp]):
                row.append(vals[tp][i])
            else:
                row.append("")
    
    return row

# Wrapper function for all function call
def run(file_path, id, entity_list):
    ext_val = {}

    g = rdflib.ConjunctiveGraph()
    g.parse(file_path, format="trig")

    ext_val = extract(g, id)

    # Dictionary for number of teachers and students 
    COUNTPROP = {
        "teachers" : 40, #Maximum number of teachers associated with a Person
        "students" : 80  #Maximum number of students associated with a Person
    }

    nlist = createList(id, ext_val, COUNTPROP)
    entity_list.append(nlist)


# Function to create CSV using master list
def createCSV(all_list):
    with open('ExtractProp1.csv', "a") as f:
        writer = csv.writer(f)
        for r in all_list:
            writer.writerow(r)


def main():
    main_list = []
    dir = os.listdir('persons')
    folder = 'persons/'
    directories = []
    for dir_name in dir:
        if dir_name.find(".git") == -1:
            directory = folder + dir_name
            directories.append(directory)
        else:
            continue

    for d in directories:    
        l = os.listdir(d)

        person_links = []
        for f in l:
            file_p = d + "/" + f
            person_links.append(file_p)

        for file in person_links:
            c = file.rsplit('/', 1)[-1]
            id = c[:-5]

            if id.find("P0RK") == -1:
                run(file, id, main_list)
            else:
                continue
        
    createCSV(main_list)
    

if __name__ == "__main__":
    main()