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Professor Yuefeng Li

Science and Engineering Faculty,
Electrical Engineering, Computer Science,
Data Science

Personal

Name
Professor Yuefeng Li
Position(s)
Professor
Science and Engineering Faculty,
Electrical Engineering, Computer Science,
Data Science
Discipline *
Artificial Intelligence and Image Processing, Information Systems
Phone
+61 7 3138 5212
Email
Location
View location details (QUT staff and student access only)
Qualifications

PhD (Deakin University)

Keywords

Data Mining, Knowledge and Data Engineering, Web Intelligence, Knowledge-based systems

* Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2008

Biography

Professor Yuefeng Li is the leader of e-Discovery Research Lab in the School of Electrical Engineering and Computer Science, QUT. He has published over 150 refereed papers (including 43 journal papers). He has demonstrable experience in leading large-scale research projects and has achieved many established research outcomes that have been published and highly cited in many significant Journals and Conferences. He has been a program chair of several International Conferences and workshops. He currently is the Managing Editor of Web Intelligence, an Associate Editor of the International Journal of Pattern Recognition and Artificial Intelligence, and an Associate Editor of the IEEE Intelligent Informatics Bulletin. Research Areas:

  • Text mining and Sentiment analysis

Topic feature discovery and opinion mining are extremely challenging topics in modern information analysis, from both an empirical and a theoretical perspective. They are also of central interest and the critical steps for many Web personalized applications and recommender systems. The problems in topic feature discovery and opinion mining have charged continuously increasing attentions from researchers in data mining, Web intelligence, text mining, machine learning, natural language processing, and information retrieval communities.

  • Data mining and Data warehousing

It is a big challenge to guarantee the quality of discovered knowledge in databases because of the huge amount of patterns and noises. The essential issue is to provide efficient methods for interpreting meaningful discovered knowledge. The research at eDiscovery Research Lab is to create new methods to improve the real performance of data mining.

  • Web Intelligence and Ontology Learning

With the explosion of information resources on the Web and corporate intranets, there is an imminent need for more effective and efficient technologies to help people deal with big data. Big data is a collection of data so large and complex that it becomes very difficult to process using traditional techniques. Big data extends beyond structured data, including unstructured data: text, audio, video, click streams, log files and more. Big data processing is not just a challenge for businesses, it is also an opportunity for businesses to become more agile and successful. The research in e-Discovery Lab is to develop techniques for dealing with big data by using text classification and ontology learning.

This information has been contributed by Professor Yuefeng Li.

Teaching

Teaching in 2011:

INB343 (INN343) – Advanced Data Mining and Data Warehousing

INB302 – IT Capstone Project

 

This information has been contributed by Professor Yuefeng Li.

Publications


For more publications by this staff member, visit QUT ePrints, the University's research repository.

Awards

Awards and recognition

Type
Academic Honours, Prestigious Awards or Prizes
Reference year
2012
Details
Best Paper Award "Personalization in Tag Ontology Learning for Recommendation Making", 14th International Conference on Information Integration and Web-Based Applications & Services, 3-5 December 2012, Ball, Indonesia.
Type
Academic Honours, Prestigious Awards or Prizes
Reference year
2008
Details
Best Paper Award, "Web information recommendation making based on item taxonomy", 10th International Conference on Enterprise Information Systems, June 2008, Barcelona, Spain, 2008.
Type
Academic Honours, Prestigious Awards or Prizes
Reference year
2007
Details
Outstanding Doctoral Thesis, "Knowledge discovery using pattern taxonomy model in text mining", Sheng-Tang Wu (Supervisors: Prof Yuefeng Li and A/Prof Yue Xu), QUT, 2007.

Research projects

Grants and projects (Category 1: Australian Competitive Grants only)

Title
Automatic Ontology Learning and Data Reasoning in Web Mining
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
DP0556455
Start year
2005
Keywords
Web intelligence Web mining Ontology learning Data mining Data reasoning Information filtering
Title
Web Services Reputation Management
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
LP0776400
Start year
2007
Keywords
Web Services, Reputation, Trust Management, Security, Quality of Service, Service Oriented Architecture
Title
Personalized Ontology Learning And Mining For Web Information Gathering
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
DP0988007
Start year
2009
Keywords
Web intelligence, Ontology learning, Ontology mining, Information gathering, Personalization,
Title
A Framework for Scalable Ontology Enrichment and Change
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
DP130102302
Start year
2013
Keywords
Knowledge Representation, Ontology Change, Description Logics
Title
Learning Specific Ontology for Un-Supervised Text Classification
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
DP140103157
Start year
2014
Keywords
Text feature extraction, Ontology learning, Data mining

Supervision