Knowledge Management and Information Retrieval

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Introduction

Knowledge Management (KM) is a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving and sharing all of an enterprise's information assets - which include databases, documents, policies and procedures and previously uncaptured expertise and experience in individual workers. KM is the process by which the organization generates wealth from its intellectual or knowledge-based assets. It is a process of leveraging and articulating skills and expertise of employees supported by IT.

Information Retrieval includes the study of theories and models; systems; techniques; content; and processes for representing, organizing, and retrieving information based on content. The advancement of the Internet has put this discipline as among important areas to study today. From the survey made by Gartner Group, Natural Language Information Retrieval is rated as number 1 in the list of top ten technologies.

Research Topic

* KM system and tools
* KM and strategy, i.e. codification, personalization, and knowledge sharing
* KM organization and culture
* KM and learning organization
* Value and ethics for knowledge-based society
* Multimedia and electronic entertainment in value and knowledge-based society
* Multimedia databases
* Knowledge brokering
* Enterprise resource management
* Enterprise workflow management
* Systems: Information Retrieval System, Text Retrieval System, Multimedia Retrieval System (data type: image, speeches, video, and music), Image Retrieval System, Intelligent Information Retrieval System and Natural Language Processing System.
* Databases: (subject application) medical, business, law etc; distributed databases; OP AC/WEbP AC; metadata; and digital libraries.
* Modes of Interaction: graphics-based visualization techniques, user-interface, relevance feedback, hypertext.
* Languages: Cross-language, Multilingual
* User interactivity: cognitive & behavioral models, collaborative retrieval
* Retrieval models and techniques: Boolean, Probabilistic, Vector, Best Match, Filtering; Concept-based, and content-based.
* Data manipulation: data mining, knowledge discovery, text mining.
* Evaluation and Testing: TREC etc.
* Performance measures: relevance, usability, satisfaction, utility, time.
* Users: training.
* Internet and WWW: SGML and XML languages; search engines.

Group Members

* Assoc Prof Dr Roslina Othman (Leader)
* Assoc Prof Dr Mohd Fauzan Noordin
* Assoc Prof Dr Nor Shahriza Abdul Karim
* Dr Abdul Rahman Ahlan
* Noor Azizah K.S. Mohamadali
* Aznan Zuhid

For further details, kindly contact Asst Prof Dr Roslina Othman (roslina@iiu.edu.my)