This module covers the rationale, techniques and application of Knowledge Graphs, such as knowledge representation, knowledge bases and schemas.
The aim is to give an introduction to Knowledge Graphs and their applications. The main goal is a semantic representation and reasoning of data using ontologies, and how to use openly available knowledge graphs such as DBpedia, Wikidata, Geonames and Wordnet. Thus, it delves into different aspects of ontology representation, creation, design, reasoning, programming and applications throughout the module.
Indicative syllabus covered in the module and its discrete elements:
- Introduction to Knowledge Graphs: Introduction to the idea of a knowledge graph, overview of available technologies for knowledge representation in graphs.
- Languages: RDF, RDFS, OWL: Describing Web resources, Resource Description Framework, data model, syntax, RDF Schema, axiomatic semantics, Web Ontology Language, requirements for ontology languages, compatibility.
- Ontology Design and Management: constructing ontologies, reusing existing ontologies, semi-automatic ontology acquisition, ontology editors and tools.
- Applied Knowledge Graphs: modelling graphs and data in practical projects.
- Reasoning: Using already existing reasoners.
- Querying with SPARQL: Rules in SPARQL, SPARQL query development, Rule ML, querying joined graphs.
- Open Source Knowledge Graphs: usage of already existing KGs such as DBpedia and Wikidata.
The module is designed to be delivered face to face for learning, teaching and assessment.
Theoretical parts about knowledge graphs as well as basic logic will be covered in lectures and tutorials. Implementation of code solutions in Python will be done in laboratory sessions.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |