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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Knowledge Graphs (INFR11215)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryRecent advances in AI have changed the perception of what AI systems can do, from decision sup-port to answering questions. An underlying feature of many AI systems concerns how knowledge is acquired, represented, and reasoned with. Today, knowledge graphs are used extensively by most of the world's leading IT companies, from search engines (e.g., the content of the Google knowledge panel is a tiny fragment of Google's knowledge graph) and chatbots to product recommenders and many applications of AI and data science. This course provides the theory and practice of knowledge graph construction, reasoning, and question answering technologies. The students will analyse case studies to construct knowledge graphs and apply reasoning services on them.
Course description In this course, we will cover topics such as:
Knowledge graph foundation and standards
- RDF (Resource Description Framework)
- OWL (Web Ontology Language)
- SPARQL (Query Language for RDF and OWL)
Knowledge graph construction, embeddings, and completion
Knowledge graph reasoning and querying
- Tableaux algorithm
- Tractable schema reasoning in EL
- Tractable query answering in DL-Lite
- Semantic parsing

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 tutorials, helping students to better understand some concepts and theories.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements Students are expected to have basic understanding of set theory and propositional / predicate logic. This is a programming light course, although one of the lab sections will use Python for knowledge graph embeddings.
Information for Visiting Students
Pre-requisitesAs above.
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  55
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 14, Seminar/Tutorial Hours 4, Supervised Practical/Workshop/Studio Hours 3, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 75 )
Assessment (Further Info) Written Exam 80 %, Coursework 20 %, Practical Exam 0 %
Additional Information (Assessment) There will be one piece of individual coursework. It will involve the student working on a specific case study where the student will be expected to construct and query a knowledge graph to solve the case study in hand. It will take around 10 hours. The length limit will be around 1000-1200 words.
Feedback During the tutorials, the students will receive formative feedback from the tutors.

The individual coursework will be about problem solving where the students will analyse a case study in depth, and they will prepare a design solution for knowledge graph construction in the chosen case study. Feedback will be provided after the student submissions are marked.

The exam will be pen and paper, and raw marks will be given.

Students are expected to attend the lab session, although the lab exercises will be designed to be doable on any DICE machine at any time. The students will get model solutions the week after the lab takes place, so as to com-pare their work to the model answers. Similarly, model answers to the tutorial exercises will be released the week after the tutorial takes place.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Knowledge Graphs (INFR11215)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. construct and query over knowledge graphs by applying relevant knowledge graph standards such as RDF, OWL and SPARQL
  2. complete knowledge graphs by applying and evaluating pros and cons of knowledge graph embeddings-based techniques
  3. reason with knowledge graphs by applying and evaluating pros and cons of description logic reasoning algorithms
  4. query over knowledge graphs by applying semantics parsing and query answering the techniques
Reading List
F. Baader,: I. Horrocks,; C. Lutz,; and U. Sattler: An Introduction to Description Logic. Cambridge University Press 2017.

R. Brachman and H. Levesque: Knowledge Representation and Reasoning. Morgan Kaufmann 2014.

J. Z. Pan, G. Vetere, J. M. Gómez-Pérez, H. Wu (Eds.): Exploiting Linked Data and Knowledge Graphs in Large Organisations. Springer 2017.

J. Z. Pan, S. Staab, U. Aßmann, J. Ebert, Y. Zhao (Eds.): Ontology-Driven Software Development. Springer 2013.

Horricks. Practical KRR:
Additional Information
Graduate Attributes and Skills Cognitive skills: problem-solving (via tutorials, coursework), critical thinking (via lectures / tutorials / coursework), handling ambiguity (via in-class discussions)
Responsibility, autonomy, effectiveness: independent learning (via readings), self-awareness and reflection (via tutorials, coursework, lectures), time management (via coursework, discussions during classes)
Communication: written communication (via coursework), verbal communication (via in class-discussions)
KeywordsKnowledge Graph,Knowledge Graph Construction,Ontology Reasoning,Query Answering
Course organiserDr Jeff Pan
Tel: (0131 6)51 5661
Course secretaryMiss Yesica Marco Azorin
Tel: (0131 6)505113
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