In an era dominated by data, the management and utilization of domain-specific language have emerged as critical challenges in various application domains, particularly those with industry-specific requirements. Our work is driven by the need to effectively manage and process large volumes of short text documents inherent in specific application domains. By leveraging domain-specific knowledge and expertise, our approach aims to shape factual data within these domains, thereby facilitating enhanced utilization and understanding by end-users. Central to our methodology is the integration of domain-specific language models with graph-oriented databases, facilitating seamless processing, analysis, and utilization of textual data within targeted domains. Our work underscores the transformative potential of the partnership of domain-specific language models and graph-oriented databases. This cooperation aims to assist researchers and engineers in metric usage, mitigation of latency issues, boosting explainability, enhancing debug and improving overall model performance. Moving forward, we envision our work as a guide AI engineers, providing valuable insights for the implementation of domain-specific language models in conjunction with graph-oriented databases, and additionally provide valuable experience in full-life cycle maintenance of this kind of products.

翻译：在数据主导的时代，领域专用语言的管理与利用已成为各应用领域（特别是具有行业特定需求的领域）面临的关键挑战。本研究源于对特定应用领域中固有海量短文本文档进行有效管理与处理的需求。通过利用领域专业知识，我们的方法旨在构建这些领域内的事实性数据框架，从而促进终端用户对数据的增强利用与理解。我们方法论的核心在于将领域专用语言模型与图数据库相集成，实现对目标领域内文本数据的无缝处理、分析与利用。本研究揭示了领域专用语言模型与图数据库协同合作的变革潜力：这种合作旨在协助研究人员和工程师优化指标使用、缓解延迟问题、提升可解释性、增强调试能力并全面提高模型性能。展望未来，我们的研究可为人工智能工程师提供实施指南，为领域专用语言模型与图数据库的联合部署提供重要见解，并为此类产品的全生命周期维护提供宝贵经验。