def METHOD_NAME(self):
    full_relation_label_list = []
    questions_for_cross_graph_training = []
    for h_r, r_iri in self.how_many_relations:
        t_1 = '''how many %s are in %s'''
        t_2 = '''what is the number of %s of %s'''
        full_relation_label = 'count_' + h_r.replace(' ', '_')
        full_relation_label_list.append(full_relation_label)
        for _CID in self.species_name_mapping:
            for name in self.species_name_mapping[_CID]:
                if name in self.iri_dict:
                    _CID = self.iri_dict[name]
                tail_entity = _CID + '_' + full_relation_label
                if tail_entity in self.full_entity_list:
                    q_1 = (t_1 % (h_r, name), _CID, 0, tail_entity.replace('\n', ''), r_iri)
                    q_2 = (t_2 % (h_r, name), _CID, 0, tail_entity.replace('\n', ''), r_iri)
                    questions_for_cross_graph_training = questions_for_cross_graph_training + [q_1, q_2]
                    self.entity_list.append(tail_entity)
    for r, r_iri in self.normal_relations:
        labels = [r]
        if r in self.synonym:
            # TODO: find alternative labels of relation
            other_r = self.synonym[r]
            labels = labels + other_r
        t_1 = '''what is the %s of %s'''
        t_2 = '''what is %s's %s'''
        full_relation_label = r.replace(' ', '_')
        full_relation_label_list.append(full_relation_label)
        for r_label in labels:
            for _CID in self.species_name_mapping:
                # TODO: replace _CID according to the dictionary value
                for name in self.species_name_mapping[_CID]:
                    if name in self.iri_dict:
                        _CID = self.iri_dict[name]
                    tail_entity = _CID + '_' + full_relation_label
                    if tail_entity in self.full_entity_list:
                        self.entity_list.append(tail_entity)
                        q_1 = (t_1 % (r_label, name), _CID, 0, tail_entity, name, r_iri)
                        q_2 = (t_2 % (name, r_label), _CID, 0, tail_entity, name, r_iri)
                        questions_for_cross_graph_training = questions_for_cross_graph_training + [q_1, q_2]
    questions_for_cross_graph_training = list(set(questions_for_cross_graph_training))
    df_pubchem_test_set = pd.DataFrame(questions_for_cross_graph_training)
    df_pubchem_test_set.columns = ["question", "head", "domain", "answer", "mention", "relation"]
    df_pubchem_test_set.to_csv(os.path.join(self.dataset_dir, 'pubchem_test.tsv'), sep='\t', index=False)
    full_relation_label_list = list(set(full_relation_label_list))
    with open(os.path.join(self.dataset_dir, "pubchem_relations"), "w") as outfile:
        outfile.write("\n".join(full_relation_label_list))