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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.03867 |
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| _version_ | 1866929529308577792 |
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| author | Di Pasquale, Ricardo Represa, Soledad |
| author_facet | Di Pasquale, Ricardo Represa, Soledad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03867 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Empowering Domain-Specific Language Models with Graph-Oriented Databases: A Paradigm Shift in Performance and Model Maintenance Di Pasquale, Ricardo Represa, Soledad Artificial Intelligence Databases Machine Learning 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. |
| title | Empowering Domain-Specific Language Models with Graph-Oriented Databases: A Paradigm Shift in Performance and Model Maintenance |
| topic | Artificial Intelligence Databases Machine Learning |
| url | https://arxiv.org/abs/2410.03867 |