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ISE Lecture SoSe2021


In this lecture, the students will learn the fundamentals of natural language processing, knowledge mining, linked data engineering, as well as information retrieval required for the development of information services.

References

  • D. Jurafsky, J.H. Martin, Speech and Language Processing, 2nd ed. Pearson Int., 2009.
  • S. Hitzler, S. Rudolph, Foundations of Semantic Web Technologies, Chapman / Hall, 2009.
  • R. Baeza-Yates, B. Ribeiro-Neto, Modern Information Retrieval, 2nd ed., Addison Wesley, 2010.

Important Dates

  • Lab Session: Mondays, 16:00 - 17:30
  • Q&A: Wednesdays, 08:00 - 09:00
  • End of Semester: July 23, 2021
Topic Lecture Lab Q & A Materials
1. Information, Natural Language, and the Web 14-Apr 19-Apr 14-Apr Download Slides View Lecture
2. Natural Language Processing 1 21-Apr 3-May 12-May Download Slides View Lecture
3. Natural Language Processing 2 28-Apr 3-May 12-May Download Slides View Lecture
4. Natural Language Processing 3 5-May 17-May 12-May Download Slides View Lecture
5. Natural Language Processing 4 12-May 17-May 12-May Download Slides View Lecture
6. Knowledge Graphs 1 21-May 31-May 16-May Download Slides View Lecture
7. Knowledge Graphs 2 26-May 7-Jun 23-Jun Download Slides View Lecture
8. Knowledge Graphs 3 2-Jun 21-Jun 23-Jun Download Slides View Lecture
9. Knowledge Graphs 4 9-Jun 21-Jun 16-May Download Slides View Lecture
10. Machine Learning 1 16-Jun 28-Jun 14-Jul Download Slides View Lecture
11. Machine Learning 2 23-Jun 28-Jun 14-Jul Download Slides View Lecture Download CSV
12. Machine Learning 3 30-Jun 12-Jul 14-Jul Download Slides View Lecture Download Wikipedia Women's Soccer Data Downloand Wikipedia Women's Soccer Data (cleanedup)
13. ISE Applications 1 7-Jul 12-Jul 21-Jul Download Slides View Lecture
14. ISE Applications 2 14-Jul 12-Jul 21-Jul Download Slides View Lecture
Summary and Q&A 21-Jul

Course Details

1. Information, Natural Language, and the Web (Download Slides)

  • 1.1 How to get Information (from the Web)?
  • 1.2 Communication, Language, and Understanding
  • 1.3 How to Measure Information?
  • 1.4 The Ever-Growing Web of Information
  • 1.5 Search Engines on the Web
  • 1.6 The Meaning of Information

2. Natural Language Processing 1 (Download Slides)

  • 2.0 What is Natural Language Processing?
  • 2.1 Basic Linguistics
  • 2.2 Morphology
  • 2.3 NLP Applications

3. Natural Language Processing 2 (Download Slides)

  • 2.4 NLP Techniques
  • 2.5 NLP Challenges
  • 2.6 Evaluation, Precision, and Recall
  • 2.7 Regular Expressions

4. Natural Language Processing 3 (Download Slides)

  • 2.8 Finite State Automata
  • 2.9 Tokenization

5. Natural Language Processing 4 (Download Slides)

  • 2.10 Language Model and N-Grams
  • 2.11 Part-Of-Speech Tagging
  • 2.12 Word Embeddings

6. Knowledge Graphs 1 (Download Slides)

  • 3.1 Knowledge Representation and Ontologies
  • 3.2 Semantic Web and the Web of Data
  • 3.3 Linked Data Principles
  • 3.4 How to identify and Access Things - URIs

7. Knowledge Graphs 2 (Download Slides)

  • 3.5 Resource Description Framework (RDF) as simple Data Model
  • 3.6 Creating new Models with RDFS
  • 3.7 Knowledge Graphs

8. Knowledge Graphs 3 (Download Slides)

  • 3.8 Querying Knowledge Graphs with SPARQL

9. Knowledge Graphs 4 (Download Slides)

  • 3.9 More Expressivity with Web Ontology Language (OWL)
  • 3.10 Knowledge Graph Programming

10. Machine Learning 1 (Download Slides)

  • 4.1 A Brief History of AI

  • 4.2 Introduction to Machine Learning

  • 4.3 Main Challenges of Machine Learning

    11. Machine Learning 2 (Download Slides)

  • 4.4 Machine Learning Workflow

  • 4.5 Basic ML Algorithms 1 - k-Means Clustering

  • 4.6 Basic ML Algorithms 2 - Linear Regression

  • 4.7 Basic ML Algorithms 3 - Decision Trees

    12. Machine Learning 3 (Download Slides)

  • 4.8 Neural Networks and Deep Learning

  • 4.9 Knowledge Graph Embeddings

  • 4.10 Knowledge Graph Completion

13. ISE Applications 1 (Download Slides)

  • 5.1 What is Information Service Engineering?
  • 5.2 Knowledge Mining and Information Extraction I
  • 5.3 Knowledge Mining and Information Extraction II
  • 5.4 Hands-on Data Analytics Example

Past Courses

  • MOOC Course: Knowledge Graphs at openHPI

WiSe20/21

  • Summer Semester 2020: Information Service Engineering Lecture
  • Winter Semester 2019/20: Information Service Engineering Project Course at KIT (AIFB)
  • Summer Semester 2019: Information Service Engineering Lecture at KIT (AIFB)
  • Winter Semester 2018/19: Information Service Engineering Project Course at KIT (AIFB)
  • Summer Semester 2018: Information Service Engineering Lecture at KIT (AIFB)
  • MOOC Course: Semantic Web at openHPI

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