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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
Not being delivered
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
Required:
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.

Recommended:
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: http://www.cs.ox.ac.uk/ian.horrocks/Publications/download/2010/HoPa10a.pdf
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
Contacts
Course organiserDr Jeff Pan
Tel: (0131 6)51 5661
Email: j.z.pan@ed.ac.uk
Course secretaryMiss Yesica Marco Azorin
Tel: (0131 6)505113
Email: ymarcoa@ed.ac.uk
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