code: from gensim.models import KeyedVectors
  import numpy as np
  import matplotlib.pyplot as plt
  import matplotlib as mpl
  import matplotlib.font_manager as fm
  import pandas as pd
  
  glove_vector_file = "vectors.txt"
  gensim_glove_vector_file = "gensim_glove_vectors.txt"
  top_k = 10
  
  words_triple_file = 'similarity_words.ttl'
  
  # GloVeの単語ベクトルファイルを読み込み、単語数とベクトルサイズを付与した処理用のファイルを作成する。
  vectors = pd.read_csv(glove_vector_file, delimiter=' ', index_col=0, header=None)
  
  vocab_count = vectors.shape[0]  # 単語数
  num_features = vectors.shape[1]  # 次元数
  
  print("単語数：{}  次元数：{}".format(vocab_count, num_features))
  
  glove_vectors = KeyedVectors.load_word2vec_format(gensim_glove_vector_file, binary=False)
  words = list(glove_vectors.vocab.keys())
  
  sim_words_list = []
  
  with open(words_triple_file, 'w') as f:
    for word in words:
      sim_words = glove_vectors.most_similar(word, [], top_k)
      
      for sim_word in sim_words:
          triple = '"{}" owl:equivalentClass "{}"'.format(word, sim_word[0])
      
          sim_words_list.append(triple)
          f.writelines(triple + '\n')
  
  
  len(sim_words_list)
  
apis: pandas.read_csv