Knowledge Base Construction


In this class, we will take an overview of Information Extraction for Knowledge Base Construction. This is the process of deriving structured information (such as alive(Elvis)) from digital text (such as the sentence "Elvis is alive"). We will first see applications of information extraction, notably in question answering systems, chatbots, and personal assistants. Then, we will cover the technical steps of knowledge base construction: natural language processing, named entity recognition, entity disambiguation, instance extraction, fact extraction, and knowledge cleaning.


The course is graded by 6 labs and a final exam. Modalities:


The course takes place on Thursday afternoon, 13:30-16:30. Due to the Corona pandemic, the course will take place online in Zoom here.
2020-11-26: Introduction
  1. Information Extraction: What is it?
  2. Information Extraction: Why do we want to do it?
  3. Information Extraction: Who does it?
  4. Information Extraction: How does it work?
  5. Knowledge Representation (until Slide 46, “equality”)
2020-12-03: Background refresher
  1. Perceptrons
  2. Feed-forward neural networks
  3. Word embeddings (only “́word2vec”)
  4. Transformers (not done, optional material)
  5. Lab: Classification (deadline: midnight on the 9th of December 2020)
2020-12-10: Named Entity Recognition and Classification
  1. Named Entity Recognition and Classification (100)
  2. Lab: NERC
2020-12-17: Typing and Disambiguation
  1. Extractive Entity Typing (20 “Hearst Patterns”)
  2. Entity Disambiguation (50-60)
  3. Lab: Disambiguation
2021-01-07: Fact extraction
  1. Fact Extraction (50)
  2. Dependency Parsing (30)
  3. Lab: Instance Extraction
2021-01-14: Fact Extraction by Reasoning
  1. Information Extraction by Reasoning (70)
  2. Lab: Max Sat
2021-01-21: Rule Mining / Link prediction
  1. Lecture
  2. Lab: Link prediction
2021-02-04: Exam (13:30-15:00)