tekom - conferences

The integration of technical documentation in Product Information Management Systems powered by Know

  • Technology Days
  • AI
  • 29. October
  • 14:50 - 15:30 PM (CET)
  • Dr. Amir Laadhar

    Dr. Amir Laadhar

    • PANTOPIX
  •  Nikhil  Acharya

    Nikhil Acharya

    • PANTOPIX

Contents

In the technical documentation domain, the challenge lies not only in managing diverse data but also in making it contextually relevant to business needs. Traditional content management systems often fall short due to their inability to provide meaningful contextual knowledge about related information within the same company. Technical documentation enhanced with knowledge graphs significantly improves decision-making. A knowledge graph-based product information management system can integrate all data sources as a single source of truth. The knowledge graph uses ontologies to structure and interpret data from various sources, including internal and external technical documentation. Business use cases, such as comparing the technical efficiency of internal products with that of competitor products, demonstrate the practical benefits. Integrating generative large language models with knowledge graphs allows for more accurate natural language question answering of business questions. 

Takeaways

Technical documentation enhanced with context from knowledge graphs improves decision-making. Additionally, LLMs grounded by these graphs provide more accurate and explainable answers to business questions.

Speakers

Dr. Amir Laadhar

Dr. Amir Laadhar

  • PANTOPIX
Biography

Dr. Amir Laadhar is a Knowledge Engineer at PANTOPIX. He applies his expertise in knowledge graphs, semantic technologies, and LLMs to create innovative solutions for data integration, knowledge extraction, and semantic search for various industrial clients in multiple domains.

 Nikhil  Acharya

Nikhil Acharya

  • PANTOPIX
Biography

Nikhil Acharya is a Knowledge Engineer at PANTOPIX · His background is in Informatics and Computer Science with experience in developing data pipelines for Knowledge Graphs using LLMs and OCR models