AH01 - Structured Data Extraction from Unstructured Web Content

Supervisor: Hofer

In many real-world applications, extracting structured data from unstructured text on web pages is a critical task. While numerous Natural Language Processing (NLP) approaches claim to handle this challenge, implementing a robust and scalable solution remains a key area of exploration. This project focuses on leveraging state-of-the-art information extraction models to build a relation extraction web service capable of transforming unstructured text into structured data.

  1. Develop a Relation Extraction Web Service:
    • Implement a web service that processes web pages as input and outputs extracted triplets (subject, predicate, object) based on a pre-defined schema.
    • Use cutting-edge NLP and information extraction techniques to ensure accuracy and scalability.
  2. Create a User-Friendly Web Application:
    • Design a front-end web application that interacts with the web service.
    • Provide an intuitive interface for users to input web pages and view the extracted structured data.

In Praktikum 1 (P1), you will extract structured data from Wikipedia articles and compare the extracted data to Wikidata. This practicum will focus on understanding the relationship between unstructured and structured data and evaluating the accuracy of the extraction process. For Praktikum 2 (P2), you will build an extraction module for FactCheck that:

  • Scrapes web page content.
  • Extracts information about individuals mentioned on the page.
  • Groups the extracted triplets by individual.
  • Uses the FactServer's compare endpoint to validate and compare the extracted information with existing data.

 

Gain hands-on experience with cutting-edge NLP and information extraction models. Learn how to bridge the gap between unstructured and structured data. Work on real-world applications like Wikipedia data analysis and conflict detection systems. Develop skills in web service development, front-end design, and system integration.

Technologies: Web Applications; Web Services; Python; Javascript; Hugging Face Transformers Library; SpaCy Library; PyTorch; Schema.org

Tags: FactCheck; Python; Information Extraction; Natural Language Processing; Web Application; Web Service; Hofer

 

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