大语言模型与知识图谱在教育AI中的结合

    import torch
    from transformers import GPT2LMHeadModel, GPT2Tokenizer
    from owlready2 import get_ontology
    
    # 1. 加载大语言模型和知识图谱
    model = GPT2LMHeadModel.from_pretrained('gpt2')
    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    onto = get_ontology("education.owl").load()
    
    # 2. 定义内容生成功能
    def generate_content(topic, max_length=200):
    # 从知识图谱中查找相关知识点
    related_concepts = [c for c in onto.classes() if topic in [str(p) for p in c.is_a]]
    
    # 拼接知识点信息作为输入
    input_text = '\n'.join([str(c) for c in related_concepts])
    
    # 利用大语言模型生成内容
    input_ids = tokenizer.encode(input_text, return_tensors='pt')
    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, do_sample=True)[0]
    generated_text = tokenizer.decode(output, skip_special_tokens=True)
    
    return generated_text
    
    # 3. 定义智能问答功能
    def answer_question(question):
    # 利用大语言模型理解问题
    input_ids = tokenizer.encode(question, return_tensors='pt')
    output = model.generate(input_ids, max_length=100, num_return_sequences=1, do_sample=True)[0]
    answer = tokenizer.decode(output, skip_special_tokens=True)
    
    # 在知识图谱中查找答案
    related_concepts = [c for c in onto.classes() if any(p in question for p in [str(p) for p in c.is_a])]
    if related_concepts:
        answer += '\n\nAdditional information from the knowledge graph:\n' + '\n'.join([str(c) for c in related_concepts])
    
    return answer
    
    # 4. 定义个性化学习辅导功能
    def provide_learning_guidance(student_profile):
    # 评估学生学习状态
    knowledge_gaps = evaluate_knowledge_gaps(student_profile)
    
    # 在知识图谱中查找相关知识点
    guidance = []
    for gap in knowledge_gaps:
        related_concepts = [c for c in onto.classes() if gap in [str(p) for p in c.is_a]]
        if related_concepts:
            guidance.append(f"To improve your understanding of '{gap}', you can focus on the following topics:")
            guidance.extend([str(c) for c in related_concepts])
            guidance.append('\n')
    
    return '\n'.join(guidance)
    
    # 5. 定义知识探索功能
    def explore_knowledge(query):
    # 利用大语言模型理解查询
    input_ids = tokenizer.encode(query, return_tensors='pt')
    output = model.generate(input_ids, max_length=100, num_return_sequences=1, do_sample=True)[0]
    interpreted_query = tokenizer.decode(output, skip_special_tokens=True)
    
    # 在知识图谱中查找相关知识点
    related_concepts = [c for c in onto.classes() if any(p in interpreted_query for p in [str(p) for p in c.is_a])]
    
    # 解释知识点之间的联系
    explanation = []
    for c1, c2 in [(c1, c2) for c1 in related_concepts for c2 in related_concepts if c1 != c2]:
        if any(c2 in [str(p) for p in c1.is_a]):
            explanation.append(f"{c1} is a type of {c2}")
        if any(c1 in [str(p) for p in c2.is_a]):
            explanation.append(f"{c2} is a type of {c1}")
    
    return '\n'.join(explanation)