Knowledge Graphs

KG: Schedule

mcorey — Fri, 04 Aug 2023 14:25:45 +0000

KG: Schedule mcorey Fri, 04/08/2023 - 15:25

Note:

(1) Our teaching assistant Ameer Saadat-Yazdi will host the Help Hour every Monday 2-3pm in IF-5.34.

(2) Our workshops will be at AT2.11, which is a lecture room without lab desktops. Thus please bring your laptop to the workshops.

Week	Duration	Lectures	Tutorials	Labs	Milestones
1	18-22, Sep 2023	L1 Introduction (Friday)			Lectures Start
2	25-29, Sep 2023	L2 Expressing Knowledge L3 KG Embeddings 1	T1 Expressing Knowledge
3	02-06, Oct 2023	L4 KG Construction L5 KG Embeddings 2
4	09-13, Oct 2023	L6 Description Logics L7 KG Embeddings 3		P1 KG Embeddings
5	16-20, Oct 2023	L8 Reasoning with Tableaux Algorithms L9 KG Embeddings 4 - Colab notebook	T2 Description Logics	P2 Knowledge Graph Construction
6	23-27, Oct 2023	No lectures this week. The mid-term Quiz (one hour, online) will be on 26th Oct. The online Quiz will be available from 10am and closed at 4pm. A trial quiz will be availlable from 10am 24th Oct to 4pm 25th Oct.			Mid-term Assessment
7	30 Oct - 3 Nov, 2023	L10 Combined KG Reasoning (Tuesday)
8	06-10, Nov 2023	L11 Querying Answering with SPARQL L12 Semantic Parsing	T3 Reasoning with Tableaux Algorithms	P3 Combined KG Reasoning
9	13-17, Nov 2023	L13 Tractable Schema Reasoning L14 Complex Query Answering	T4 Tractable Schema Reasoning	P4 SPARQL	Final week for Lectures
10	20-24, Nov 2023	Guest Lecture: High-Performance Reasoning over Knowledge Bases Trigger Graphs (Friday)		P5 Complex Question Answering	Final week for Tutorials and Lab Practicals
11	27 Nov - 1 Dec, 2023
12	04-08, Dec 2023
13	11-15, Dec, 2023
14	18-22, Dec 2023	Exam week (Appendix)

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KG: Course Contacts

flittlet — Thu, 03 Aug 2023 15:38:50 +0000

KG: Course Contacts flittlet Thu, 03/08/2023 - 16:38

Course organiser: Jeff Pan
Course secretary: Yesica Marco Azorin, ymarcoa@ed.ac.uk
Lecturers:
- Jeff Pan
- Pasquale Minervini

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KG: Assessment

flittlet — Thu, 03 Aug 2023 15:38:47 +0000

KG: Assessment flittlet Thu, 03/08/2023 - 16:38

All assessments should be accessed via Learn .

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KG: Resource List

flittlet — Thu, 03 Aug 2023 15:38:43 +0000

KG: Resource List flittlet Thu, 03/08/2023 - 16:38

KG: Course Materials

flittlet — Thu, 03 Aug 2023 15:38:41 +0000

KG: Course Materials flittlet Thu, 03/08/2023 - 16:38

Lecture Recordings

All lecture recordings should be accessed via Learn; you will need to log in using your EASE account. (Learn provides you with access to any lecture recordings available for this course. You will need to select the "lecture recording" link once, before you can access any direct links to a lecture recording.)

Lectures Slides

All lecture slides can be found in the Schedule page.

Tutorial Materials

All tutorial matereials can be found in the Schedule page.

Lab Materials

All lab materials can be found in the Schedule page.

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KG: Knowledge Graphs

flittlet — Thu, 03 Aug 2023 15:38:40 +0000

KG: Knowledge Graphs flittlet Thu, 03/08/2023 - 16:38

Welcome to Knowledge Graphs course

Course organiser: Jeff Pan

Course Outline

In this course, we will cover the following 3 aspects of knowledge graphs:
1. Logical foundation of knowledge representation, reasoning and related standards
- Expressing knowledge and knowledge graph construction
- DL reasoning
- Key standards: RDF, OWL and SPARQL (Query Language for RDF and OWL)
2. Graph learning and embeddings
- Knowlege graph embeddings
- Complex query answering over knowledge graphs
3. Natural language processing
- Semantic parsing

Learning Outcomes

On successful completion of this course, you should be able to:

construct and query over knowledge graphs by applying relevant knowledge graph standards such as RDF, OWL and SPARQL
complete knowledge graphs by applying and evaluating pros and cons of knowledge graph embeddings-based techniques
reason with knowledge graphs by applying and evaluating pros and cons of description logic reasoning algorithms
query over knowledge graphs by applying semantics parsing and query answering the techniques

The students will be expected to prepare for the lectures by reading related textbook chapters and papers. In addition to lectures, there will be some tutorial and lab sessions, helping students to better understand some concepts and theories.

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