将大型语言模型与企业知识图相结合:对增强自然语言理解的视角.
Luca Mariotti1, Veronica Guidetti1, Federica Mandreoli1
1Department of Physics, Informatics and Mathematics, Università di Modena e Reggio Emilia, Modena, Italy.
Frontiers in artificial intelligence
|September 11, 2024
概括
大型语言模型 (LLM) 为丰富知识图 (KG) 提供先进的解决方案,自动化复杂的流程. 这项研究探讨了基于LLM的KG丰富技术和工业部署的挑战.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 知识表示 知识表示
背景情况:
- 知识图 (KG) 组织实体和关系,增强推和问答系统等应用程序.
- 企业 KG,如 Expert.AI 的 Sensigrafo,使用自然语言理解 (NLU) 来创建面向机器的词典.
- 传统上,KG的维护和丰富需要大量的手工劳动.
研究的目的:
- 审查知识图表丰富 (KGE) 的最新大型语言模型 (LLM) 技术.
- 识别在工业环境中基于LLM的KGE自动化和部署的挑战.
- 为数据质量,稀缺性,经济可行性,隐私和自动化KGE中的语言演变提出解决方案.
主要方法:
- 审查目前基于LLM的知识图表丰富 (KGE) 方法.
- 分析工业应用和KGE流程自动化方面的挑战.
- 讨论应对数据,经济,隐私和语言问题的战略.
主要成果:
- 法律学显示出自动化知识图表丰富 (KGE) 的巨大潜力.
- 工业部署面临的障碍包括数据质量,可扩展性,成本,隐私和不断变化的语言.
- 自动化KGE需要平衡准确性与实际实施问题.
结论:
- 基于LLM的KGE是推动知识表示和AI应用的有希望的前沿.
- 克服工业挑战对于广泛采用自动化KGE至关重要.
- 未来的工作应该集中在强大的,准确的,经济可行的自动化KGE解决方案上.
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