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Telecom — Knowledge Extraction from Scientific Documents and Data

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This DAG extracts valuable knowledge from scientific documents and databases using Named Entity Recognition techniques. It ensures data accuracy and facilitates efficient access to critical information in the telecom sector.

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Overview

The primary purpose of the telecom_kmds_knowledge_extraction DAG is to harness advanced Named Entity Recognition (NER) techniques to identify and classify relevant information from scientific documents and databases within the telecom industry. The architecture is designed to ingest diverse data sources, including research papers, technical reports, and internal databases. The ingestion pipeline begins with the collection of these documents, followed by pre-processing steps that involve text nor

The primary purpose of the telecom_kmds_knowledge_extraction DAG is to harness advanced Named Entity Recognition (NER) techniques to identify and classify relevant information from scientific documents and databases within the telecom industry. The architecture is designed to ingest diverse data sources, including research papers, technical reports, and internal databases. The ingestion pipeline begins with the collection of these documents, followed by pre-processing steps that involve text normalization and tokenization to prepare the data for analysis. Once the data is prepared, the NER algorithms are applied to extract entities such as technologies, standards, and metrics from the text. Quality control measures are integrated into the workflow to ensure the accuracy of the extractions, including validation against predefined criteria and manual review processes. The extracted information is then structured and stored in a knowledge graph, which facilitates quick search and retrieval capabilities for users. Monitoring key performance indicators (KPIs) such as extraction accuracy, processing time, and user engagement are essential for assessing the effectiveness of the DAG. By providing a centralized repository of extracted knowledge, this DAG delivers significant business value by enabling telecom companies to leverage scientific insights for decision-making, innovation, and competitive advantage.

Part of the Scientific ML & Discovery solution for the Telecom industry.

Use cases

  • Enhances decision-making with timely access to scientific knowledge
  • Improves operational efficiency through automated data extraction
  • Reduces manual effort in data processing and analysis
  • Supports innovation by providing insights into emerging technologies
  • Strengthens competitive positioning with data-driven strategies

Technical Specifications

Inputs

  • • Research papers from telecom conferences
  • • Technical reports from industry standards organizations
  • • Internal databases of telecom technologies
  • • Patent filings related to telecom innovations
  • • Market research documents on telecom trends

Outputs

  • • Structured knowledge graph of extracted entities
  • • Summary reports of key findings and insights
  • • Performance metrics dashboard for monitoring
  • • Validated extraction accuracy reports
  • • User access logs for knowledge retrieval

Processing Steps

  1. 1. Collect scientific documents and data sources
  2. 2. Pre-process text for normalization and tokenization
  3. 3. Apply Named Entity Recognition algorithms
  4. 4. Conduct quality control checks on extracted data
  5. 5. Store extracted entities in a knowledge graph
  6. 6. Generate performance metrics and user reports
  7. 7. Facilitate user access to the knowledge graph

Additional Information

DAG ID

WK-0401

Last Updated

2025-05-25

Downloads

92

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