ISE Lecture SoSe2021

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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
2. Natural Language Processing 1	21-Apr	3-May	12-May
3. Natural Language Processing 2	28-Apr	3-May	12-May
4. Natural Language Processing 3	5-May	17-May	12-May
5. Natural Language Processing 4	12-May	17-May	12-May
6. Knowledge Graphs 1	21-May	31-May	16-May
7. Knowledge Graphs 2	26-May	7-Jun	23-Jun
8. Knowledge Graphs 3	2-Jun	21-Jun	23-Jun
9. Knowledge Graphs 4	9-Jun	21-Jun	16-May
10. Machine Learning 1	16-Jun	28-Jun	14-Jul
11. Machine Learning 2	23-Jun	28-Jun	14-Jul
12. Machine Learning 3	30-Jun	12-Jul	14-Jul
13. ISE Applications 1	7-Jul	12-Jul	21-Jul
14. ISE Applications 2	14-Jul	12-Jul	21-Jul
Summary and Q&A	21-Jul

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Course Details

• All Slides

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

• 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 ()

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

3. Natural Language Processing 2 ()

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

4. Natural Language Processing 3 ()

• 2.8 Finite State Automata
• 2.9 Tokenization

5. Natural Language Processing 4 ()

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

6. Knowledge Graphs 1 ()

• 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 ()

• 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 ()

• 3.8 Querying Knowledge Graphs with SPARQL

9. Knowledge Graphs 4 ()

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

10. Machine Learning 1 ()

• 4.1 A Brief History of AI

• 4.2 Introduction to Machine Learning

• 4.3 Main Challenges of Machine Learning

  11. Machine Learning 2 ()

• 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 ()

• 4.8 Neural Networks and Deep Learning

• 4.9 Knowledge Graph Embeddings

• 4.10 Knowledge Graph Completion

13. ISE Applications 1 ()

• 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

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Past Courses

Knowledge Graphs (2020)

• MOOC Course: Knowledge Graphs at openHPI

WiSe20/21

• Information Service Engineering Project Course at KIT (AIFB)
• Representation Learning for Knowledge Graphs at KIT (AIFB)

SoSe2020 - ISE Lecture

• Summer Semester 2020: Information Service Engineering Lecture

WiSe19/20 - ISE Project Course

• Winter Semester 2019/20: Information Service Engineering Project Course at KIT (AIFB)

SoSe2019 - ISE Lecture

• Summer Semester 2019: Information Service Engineering Lecture at KIT (AIFB)

WiSe18/19 - ISE Project Course

• Winter Semester 2018/19: Information Service Engineering Project Course at KIT (AIFB)

SoSe2018 - ISE Lecture

• Summer Semester 2018: Information Service Engineering Lecture at KIT (AIFB)

Semantic Web (2017)

• MOOC Course: Semantic Web at openHPI

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ISE Team

• Prof. Dr. Harald Sack
• Jörg Waitelonis (Post-Doc)
• Oleksandra Bruns (Junior Researcher, PhD candidate)
• Genet Asefa Gesese (Junior Researcher, PhD candidate)
• Fabian Hoppe (Junior Researcher, PhD candidate)
• Mary Ann Tan (Junior Researcher, PhD candidate)
• Tabea Tietz (Junior Researcher)
• Mahsa Vafaie (Junior Researcher, PhD candidate)