# Code Implementation: Generating Text Embeddings          import numpy as np          from gensim.models import Word2Vec                   # Sample sentences          sentences = [              ["I", "love", "natural", "language", "processing"],              ["Text", "embeddings", "are", "fascinating"],              ["NLP", "makes", "computers", "understand", "language"]          ]                   # Train Word2Vec model          model = Word2Vec(sentences, vector_size=5, window=5, min_count=1, sg=1)          # Get embeddings for words          word_embeddings = {}          for word in model.wv.index_to_key:              word_embeddings[word] = model.wv[word]                   # Print embeddings          for word, embedding in word_embeddings.items():              print(f"Embedding for '{word}': {embedding}")

Embedding for'I': [-0.01978252  0.02348454 -0.0405227  -0.01806103  0.00496107]          Embedding for'love': [ 0.01147135 -0.00716509 -0.02319919  0.03274594 -0.00713439]          Embedding for'natural': [ 0.03319094  0.02570618  0.02645341 -0.00284445 -0.01343429]          Embedding for'language': [-0.01165106 -0.02851446 -0.01676577 -0.01542572 -0.02357706]          Embedding for'processing': [-0.00205235  0.01240269 -0.03660049 -0.0240654  -0.03612582]          Embedding for'Text': [ 0.02553515  0.03493764  0.01932768 -0.02028572  0.02185934]          Embedding for'embeddings': [ 0.01769094  0.02836292 -0.02720205 -0.01580047 -0.0323391 ]          Embedding for'are': [ 0.01449668  0.0178032   0.02154245 -0.02403528 -0.03612522]          Embedding for'fascinating': [ 0.0389471   0.00991404  0.0198368  -0.02466156 -0.03446501]          Embedding for'NLP': [ 0.00828243 -0.02125732  0.01082581  0.02250805  0.02270168]          Embedding for'makes': [ 0.01696491  0.0151721  -0.02186292 -0.01510419 -0.02021307]          Embedding for'computers': [ 0.00983663 -0.02679762  0.03002482 -0.02373362 -0.01307139]          Embedding for'understand': [-0.0326019   0.01835899  0.01538064 -0.01008516  0.01717436]    

# Code Implementation: Tokenization and Encoding          from transformers import BertTokenizer, BertModel                   # Initialize BERT tokenizer and model          tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')          model = BertModel.from_pretrained('bert-base-uncased')                    # Tokenize and encode the input query          query = "What is the capital of France?"          input_ids = tokenizer.encode(query, add_special_tokens=True, return_tensors="pt")

{              'input_ids': tensor([[  101,  2054,  2003,  1996,  3007,  1997,  2605,  1029,   102,     0]]),              'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]),              'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])          }

# Code Implementation: Retrieval and Similarity Matching          from sklearn.metrics.pairwise import cosine_similarity                  # Retrieve passages and compute similarity scores          query_embedding = model(input_ids)[0].mean(dim=1).detach().numpy()          passage_embeddings = ...  # Retrieve passage embeddings          similarity_scores = cosine_similarity(query_embedding, passage_embeddings)

similarity_scores = [0.75, 0.82, 0.65]

# Select passage with highest similarity score          max_similarity_index = np.argmax(similarity_scores)          selected_passage = passages[max_similarity_index]                    # Output selected passage and similarity score          print("Selected Passage:")          print(selected_passage)          print("Similarity Score:", similarity_scores[0][max_similarity_index])

Selected Passage:          "Barack Obama served as the 44th president of the United States."          Similarity Score:0.82

from openie import StanfordOpenIE                    text = "Hawaii is a state in the United States. Barack Obama served as the 44th president of the United States. The Louvre Museum is located in Paris, France."                  with StanfordOpenIE() as client:              triples = client.annotate(text)              for triple in triples:                  print(triple)                        cleaned_triples = [(subject.lower(), relation.lower(), object.lower()) for (subject, relation, object) in triples]              print("Cleaned Triples:", cleaned_triples)

('Hawaii', 'is', 'a state in the United States')          ('Barack Obama', 'servedas', 'the44th president of the United States')          ('The Louvre Museum', 'is located in', 'Paris, France')                  Cleaned Triples: [('hawaii', 'is', 'a state in the united states'), ('barack obama', 'servedas', 'the44th president of the united states'), ('the louvre museum', 'is located in', 'paris, france')]

import networkx as nx                   # Create a directed graph          knowledge_graph = nx.DiGraph()                   # Add nodes and edges from cleaned triples          for (subject, relation, object) in cleaned_triples:              knowledge_graph.add_edge(subject, object, relation=relation)                   # Visualize the knowledge graph          nx.draw(knowledge_graph, with_labels=True)    

from pykeen.pipeline import pipeline          

          # Define pipeline configuration          pipeline_config = {              "dataset": "nations",              "model": {                  "name": "TransE",                  "embedding_dim": 50              },              "training": {                  "num_epochs": 100,                  "learning_rate": 0.01              }          }          
          # Create and run pipeline          result = pipeline(              **pipeline_config,              random_seed=1234,              use_testing_data=True          )          
          # Access trained embeddings          entity_embeddings = result.model.entity_embeddings          relation_embeddings = result.model.relation_embeddings                  # Print embeddings          print("Entity Embeddings:", entity_embeddings)          print("Relation Embeddings:", relation_embeddings)    这是输出：Entity Embeddings: [Embedding dimension:(120, 50)]          Relation Embeddings: [Embedding dimension:(120, 50)]

“Hawaii”: [0.1, -0.2, 0.5, …]“United States”: [0.3, 0.4, -0.1, …]“Barack Obama”: [-0.2, 0.6, 0.2, …]“Louvre Museum”: [-0.5, 0.1, 0.7, …]

“is a state in”: [0.2, -0.3, 0.1, …]“served as”: [0.4, 0.2, -0.5, …]“is located in”: [-0.1, 0.5, 0.3, …]    

import numpy as np          from sklearn.metrics.pairwise import cosine_similarity          

          # Sample knowledge embeddings          knowledge_embeddings = { } #initialise the above knowledge embeddgings output          

          # Sample text embeddings          text_embeddings = { } #initialise the above text embeddgings output                       # Consider passages from knowledge base          knowledge_base = {              "Passage 1": "Hawaii is a state in the United States.",              "Passage 2": "Barack Obama served as the 44th president of the United States.",              # Add more passages as needed          }          

          # Function to combine text and knowledge embeddings          def combine_embeddings(text_emb, know_emb):              combined_emb = {}              for entity, t_emb in text_emb.items():                  if entity in know_emb:                      combined_emb[entity] = np.concatenate([t_emb, know_emb[entity]])                  else:                      combined_emb[entity] = t_emb              return combined_emb          
          # Function to retrieve relevant passages using combined embeddings          def retrieve_passages(query_emb, knowledge_base_emb):              similarities = {}              for passage, kb_emb in knowledge_base_emb.items():                  sim = cosine_similarity([query_emb], [kb_emb])[0][0]                  similarities[passage] = sim              sorted_passages = sorted(similarities.items(), key=lambda x: x[1], reverse=True)              return sorted_passages                    # Example usage          combined_embeddings = combine_embeddings(text_embeddings, knowledge_embeddings)          query = "query"          relevant_passages = retrieve_passages(combined_embeddings[query], knowledge_embeddings)          

          # Print relevant passages          for passage, similarity in relevant_passages:              print("Passage:", passage)              print("Similarity:", similarity)              print()   

Passage: Passage 1          Similarity: 0.946943628930774          
          Passage: Passage 2          Similarity: 0.9397945401928656