OneKE: A Docker-based System for Knowledge-Based Extraction using LLM Agents and Schema Guidance

Extracting knowledge from unstructured data represents a central challenge in modern data processing. OneKE, a new, Docker-based system, addresses this challenge by combining Large Language Models (LLMs) with an agent-based approach and schema guidance. This system enables the automated extraction of information from various sources such as text documents, images, and other data types, and organizes this information according to a predefined schema.

How OneKE Works

OneKE is based on the idea of using LLMs as agents that independently search for, extract, and process information. Schema guidance plays a crucial role in this process. A predefined schema, which defines the structure of the information to be extracted, serves as a guide for the LLM agents. This ensures that the extracted information is consistent and structured and can be directly integrated into downstream applications.

The Docker-based architecture of OneKE offers several advantages. First, it allows for easy deployment and scaling of the system. Second, containerization ensures the reproducibility of results, as all dependencies and configurations are encapsulated within the Docker container. Third, the Docker architecture facilitates the integration of OneKE into existing data science pipelines.

Applications of OneKE

The flexibility and scalability of OneKE open up a variety of application possibilities. These include:

- Knowledge Management: OneKE can be used to extract knowledge from various sources and store it in a central knowledge database. - Market Research: OneKE can help identify market trends and customer needs from unstructured data such as social media posts and customer reviews. - Business Intelligence: OneKE can support companies in gaining valuable insights from their data and making data-driven decisions. - Research and Development: OneKE can assist scientists in analyzing large amounts of data and identifying relevant information.

OneKE Compared to Other Systems

Compared to traditional methods of information extraction, OneKE offers several advantages. The agent-based approach allows for more flexible and adaptive extraction of information. Schema guidance ensures consistency and structure of the extracted data. The Docker-based architecture simplifies the deployment and scaling of the system. Compared to other LLM-based systems, OneKE is distinguished by the combination of these three core components: agent-based architecture, schema guidance, and Docker containerization.

Future Developments

The development of OneKE is an ongoing process. Future work focuses on improving the efficiency and accuracy of information extraction, as well as expanding the supported data types and languages. Another focus is on the development of intuitive user interfaces to make OneKE accessible to a wider audience.

OneKE represents a promising approach for knowledge-based extraction from unstructured data. The combination of LLM agents, schema guidance, and Docker-based architecture provides a powerful and flexible tool for a variety of applications. With the ongoing development of LLMs and the improvement of the underlying technologies, OneKE will play an even more important role in data processing in the future.

Bibliography: Chang, Kai-Wei et al. “RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System.” Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations. 2021. Papers with Code. “RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System.” ResearchGate. “RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System.” University of Illinois Experts. “RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System.” University of Pennsylvania. “Kairos/RESIN System.” rochacbruno/my-awesome-stars GitHub repository. hiifong/starList GitHub repository. dailyr.netlify.app archives.