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Much of the world's knowledge is stored in the form of unstructured data (e.g. text) or implicitly in structured data (e.g. databases). In this subject students will learn algorithms and data structures for extracting, retrieving and analysing explicit knowledge from various data sources, with a focus on the web. Topics include: data encoding and markup, web crawling, regular expressions, document indexing, text retrieval, clustering, classification and prediction, pattern mining, and approaches to evaluation of knowledge technologies.
Introduction to Knowledge Technologies; String search; Genomics; Text processing and search; Web search and retrieval; Introduction to Data Mining; Introduction to basic Probability; Classification; Association Rules; Clustering; Evaluation measures.
Examples of projects that students may completed are:
- A method for automatically predicting the geo-location of a Twitter user on the basis of their posts
- An automatic method for tagging multilingual Wikipedia documents with Wikipedia categories
- A search engine for Twitter data, which takes into account the time stamp of the query and documents
- A search engine for web user forum data
- A search engine servicing mixed monolingual queries (as in monolingual queries from a range of languages) over a large-scale document collection
- Classification and prediction of some real world problems using machine learning techniques.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILO)
Having completed this unit the student is expected to describe and apply the fundamentals of knowledge systems, including data acquisition and aggregation, knowledge extraction, text retrieval, machine learning and data mining.
On completion of this subject the student is expected to:
- Gain an understanding of a representative selection of knowledge technology techniques in both theoretical and applied contexts
- Develop familiarity with component technologies used in commonly-deployed knowledge technology systems
- Get a feel for what research is all about, especially relating to knowledge technology-related projects underway at The University of Melbourne
On completion of this subject, students should have the following generic skills:
- General skills include the ability to undertake problem identification, formulation, and developing solutions especially exploiting acquired data
- In addition this subject exposes students to use various data processing tools and make them learn integration of these tools to build more complex software systems
- As a result the student will develop skills to utilise a systems approach to complex problems.
Last updated: 2 December 2019