How we structured historical medicinal data into a knowledge graph with AI

Continue Reading

Additionally, much of the information existed in unstructured formats—handwritten manuscripts, ancient scriptures, and symbol-based texts—further complicating data extraction and analysis. Ensuring accurate interpretation while maintaining the integrity of medicinal knowledge was a critical hurdle.

Solution: AI-powered framework for structuring historical medicinal literature into a knowledge graph

To address the challenges, we conducted thorough research and explored multiple AI methodologies to determine the most effective strategy for extracting, structuring, and retrieving historical medicinal knowledge. Our proposed approach includes:

End-to-End AI Pipeline

We outlined a systematic pipeline to streamline the entire process—from extracting handwritten and scanned texts to node placement within the knowledge graph. This ensures a seamless flow of data acquisition, transformation, and validation at each stage.

Data Extraction from Unstructured Sources

We proposed leveraging fine-tuned AI models to extract text from handwritten and scanned documents. These models would include four possible architectures, adapting to the complexity in the underlying dataset and the structure it is presented in. Additionally, multilingual processing models would be optimized to ensure accurate extraction across various historical sources.

Standardization & Semantic Structuring

To make the extracted data more accessible, we suggested using fine-tuned AI models, pretrained on specialized datasets, furthering their ability to modernize and standardize the language. These models would perform semantic translation while ensuring terminological consistency and standardization across multiple languages and historical sources.

Knowledge Graph Construction

The plan is to identify key medical entities and their relationships, extract triplets to create structured data representations, and utilize pre-trained domain-specific models to improve accuracy.

AI Chatbot for Knowledge Retrieval

Our solution involves developing an AI chatbot that uses the user’s question to traverse the knowledge graph, identifying key attributes and relationships between entities. This enables the chatbot to deliver comprehensive, contextually relevant answers to user query, ensuring precise and meaningful information retrieval.

Benefits: Clear knowledge graph roadmap, efficient data extraction, and improved accessibility

Our research and strategic roadmap provide Sensorium with a structured approach to harness AI for historical medical text analysis and drug discovery.

This research-driven approach enables the client to:

• Efficiently process and analyze historical medical texts across multiple languages, gaining valuable insights for research.
• Identify overlooked therapeutic compounds that could lead to breakthrough drug discoveries.
• Create a structured, AI-powered knowledge database, transforming fragmented medical records into a searchable resource.
• Accelerate drug discovery by leveraging centuries of documented medical practices to drive innovation.
• Establish a scalable research framework, ensuring continuous advancements in plant-based drug development.

This research-backed strategy equips the client to tackle complex challenges, with zeb’s expertise guiding the next steps forward.

Partner with zeb today

At zeb, we understand the core business challenges and break down the complexities involved. With a research and data-driven approach, we provide a strategic roadmap and deliver bespoke solutions that address unique challenges, driving impactful results.