| CODE | ARI5124 | ||||||
| TITLE | Enterprise Knowledge Management | ||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||
| MQF LEVEL | 7 | ||||||
| ECTS CREDITS | 5 | ||||||
| DEPARTMENT | Artificial Intelligence | ||||||
| DESCRIPTION | This unit presents students with an in-depth study of scalable solutions to manage, process and analyse enterprise-level Knowledge Graphs. In particular students will be exposed to the foundations, techniques, and algorithms for building and querying knowledge graphs. Students will be provided with the foundation to applying data governance as an approach to better manage and exploit data and knowledge in an enterprise environment. Topics include Semantic Web, definition of graph schemas, concept interlinking, querying knowledge graphs, concept detection and inferencing, federated querying, data value chains, value creation, and data governance. Study-unit Aims: Through this study -unit, students will be given the opportunity to: - learn the foundations of Semantic Web Technologies; - learn the relevant techniques used for interlinking, federated querying, reasoning over structured data; - learn how to design, construct, store and query knowledge graphs; - learn how to apply big data tools and infrastructure (e.g., Spark) to build and query knowledge graphs; - learn how to govern data and manage data value chains; - learn and work both independently and within groups; - develop balance between theoretical and practical skills. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - gain an in depth understand of the data structures used in the context of knowledge graphs; - analyse and evaluate the challenges behind knowledge graph creation; - create, process and manage large knowledge graphs; - critically understand data governance, data value chains and the principles behind them. 2. Skills By the end of the study-unit the student will be able to: - employ big data technologies to design, construct and query enterprise-level knowledge graphs; - design effective techniques to combine and query data from structured and unstructured data sources; - employ techniques for federated querying over local and external knowledge graphs; - employ techniques to effectively interlink and reason over structured data; - design a data value chain based on a real-life use case, optimised through the definition of value creation processes; - design a data governance approach for a real-life use case. Main Text/s and any supplementary readings: - Exploiting Linked Data and Knowledge Graphs in Large Organisations. Jeff Z. Pan, Guido Vetere, Jose Manuel Gomez-Perez and Honghan Wu. ISBN 978-3-319-45652-2. - DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition). Dama International. ISBN-13: 978-1634622349. |
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| ADDITIONAL NOTES | Pre-Requisite Study-unit: ICS5111 | ||||||
| STUDY-UNIT TYPE | Lecture and Independent Study | ||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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