def METHOD_NAME(self):
    property_dict = {"SpeciesGeometry_latent": "geometry",
                     "RotationalRelaxationCollisionNumber_latent": "rotational relaxation collision number",
                     "Polarizability_latent": "polarizability",
                     "LennardJonesDiameter_latent": "lennard jones diameter",
                     "LennardJonesWellDepth_latent": "lennard jones well depth",
                     "DipoleMoment_latent": "dipole moment"
                     }
    df_train = pd.read_csv(os.path.join(self.dataset_path, "ontokin-train.txt"), sep='\t', header=None)
    question_list = []
    for idx, row in df_train.iterrows():
        # question	head	tail	rel
        h, r, t = row
        if "_latent" in r:
            h_idx = (self.entity2idx[h])
            r_idx = (self.relation2idx[r])
            t_idx = (self.entity2idx[t])
            q = property_dict[r]
            question_list.append((q, h_idx, t_idx, r_idx))
    df_questions = pd.DataFrame(question_list)
    df_questions.columns = ["question", "head", "tail", "rel"]
    df_questions.to_csv(os.path.join(self.dataset_path, "score_model_training.tsv"), sep='\t')