High Tech — Scientific Document and Patent Knowledge Extraction Pipeline

Overview

The primary purpose of this DAG is to extract valuable knowledge from scientific documents and patents by utilizing Named Entity Recognition (NER) and taxonomy techniques. The data sources include a variety of scientific publications, patent filings, and research articles. The ingestion pipeline begins with the collection of these documents, followed by preprocessing steps to ensure data integrity and uniformity. During the processing phase, NER algorithms identify and categorize entities such a

The primary purpose of this DAG is to extract valuable knowledge from scientific documents and patents by utilizing Named Entity Recognition (NER) and taxonomy techniques. The data sources include a variety of scientific publications, patent filings, and research articles. The ingestion pipeline begins with the collection of these documents, followed by preprocessing steps to ensure data integrity and uniformity. During the processing phase, NER algorithms identify and categorize entities such as authors, institutions, and key terms, while taxonomy mapping establishes relationships between these entities. Quality control measures are implemented throughout the pipeline, including validation checks to ensure the accuracy of the extracted data. The outputs of this DAG consist of a structured knowledge graph that facilitates efficient information retrieval and discovery. Key performance indicators (KPIs) monitored include the successful extraction rate and the average processing time per document, which are crucial for assessing the efficiency of the pipeline. The business value of this DAG lies in its ability to streamline research processes, enhance data accessibility, and support informed decision-making in the high-tech industry, ultimately leading to accelerated innovation and competitive advantage.

Part of the Scientific ML & Discovery solution for the High Tech industry.

Use cases

• Accelerates research and development cycles in high-tech.
• Enhances the accuracy of information retrieval processes.
• Supports informed decision-making with reliable data.
• Promotes collaboration through shared knowledge resources.
• Drives innovation by uncovering hidden insights in data.