INFM 700 Information Architecture (Spring 2008)

Team Presentations

1. Overview

This assignment will be completed in teams of three individuals. Together, each team will be responsible for a 30-40 minute class presentation about a particular topic. The topics are divided into two broad themes, and will occur on two separate dates.

Theme #1: Survey of Knowledge Resources (Presentation date: 2/25)

• Medical Subject Headings (MeSH)
• Getty Art and Architecture Thesaurus (AAT)
• Folksonomies (e.g., del.icio.us) and community-built directories (e.g., Open Directory Project)
• WordNet and FrameNet

Theme #2: Techniques for Unstructured Information (Presentation date: 3/24)

• Recommender systems
• Mining search engine query logs
• Automatic clustering and classification
• Image Search

Each presentation will be divided into two portions:

• A lecture, lasting approximately 15-20 minutes.
• A discussion, lasting approximately 15-20 minutes.

In the lecture portion of the presentation, I would like you to cover specific issues, outlined in Section 2.

In the discussion portion of the presentation, you will be responsible for leading a class discussion on your topic as it pertains to issues in information architecture. This involves:

• Coming up with relevant questions to initiate the discussion.
• Appropriately steering the direction of the discussion.
• Finding ways to energize the discussion if it falters (e.g., injecting new questions).

You are free to divide up the preparation effort in any way that you see fit. However, I want everyone on your team to be equally engaged.

2. Specific Issues to Address

Here are the specific questions I would like you to address for topics under theme #1, "Survey of Knowledge Resources":

• What is it?
• What is the underlying theory?
• How was it created and how is it maintained?
• How can it be applied to organize information?
• What is its relevance to information architecture?

Here are the specific questions I would like you to address for topics under theme #2, "Techniques for Unstructured Information":

• What is it?
• How does it work?
• It is commonly used today? If so, where and in what contexts?
• What is its relevance to information architecture?
• What is in store for the future?

For both sets of topics, the above questions serve as starting points. Feel free to elaborate in ways which you think are appropriate to the specific topic.

3. Starting Points

For each topic, I have provided below a few references to serve as starting points for research. You are expected to do additional research beyond these suggestions.

Medical Subject Headings (MeSH)

• Homepage at the National Library of Medicine
• H. J. Lowe and G. O. Barnett. Understanding and Using the Medical Subject Headings (MeSH) Vocabulary to Perform Literature Searches. JAMA, 271(14):1103-1108, 1994.
• Margaret H. Coletti and Howard L. Bleich. Medical Subject Headings Used to Search the Biomedical Literature. JAMIA, 8:317-323, 2001.

Getty Art and Architecture Thesaurus (AAT)

• Overview at the Getty Research Institute
• Dagobert Soergel. The Art and Architecture Thesaurus (AAT): A Critical Appraisal. Visual Resources, 10(4):369-400, 1995.

Folksonomies and community-built directories

• del.icio.us
• flickr
• Open Directory Project

WordNet and FrameNet

• WordNet homepage at Princeton
• FrameNet homepage at Berkeley
• George Miller. WordNet: A Lexical Database for English. Communications of the ACM, 38(11):49-51, 1995
• Collin F. Baker, Charles J. Fillmore, and John B. Lowe. The Berkeley FrameNet Project. Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics (COLING/ACL 1998).

Recommender systems

• Gediminas Adomavicius and Alexander Tuzhilin. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734-749, 2005
• Greg Linden, Brent Smith, and Jeremy York. Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 7(1):76-80, 2003.

Mining search engine query logs

• Hang Cui, Ji-Rong Wen, Jian-Yun Nie, and Wei-Ying Ma. Query Expansion by Mining User Logs. IEEE Transactions on Knowledge and Data Engineering, 15(4):829-839, 2003.
• Steven M. Beitzel, Eric C. Jensen, David D. Lewis, Abdur Chowdhury, and Ophir Frieder. Automatic Classification of Web Queries Using Very Large Unlabeled Query Logs. ACM Transactions on Information Systems, 25(2):1-29, 2007.

Automatic clustering and classification

• Marti A. Hearst. Clustering Versus Faceted Categories for Information Exploration. Communications of the ACM, 49(4):59-61, 2006.
• Marti A. Hearst. The Use of Categories and Clusters for Organizing Retrieval Results, in Natural Language Information Retrieval, Strzalkowski (ed.), Kluwer Academic Publishers, 1999.

Image search

• Y. Alp Aslandogan and Clement T. Yu. Techniques and Systems for Image and Video Retrieval. IEEE Transactions on Knowledge and Data Engineering, 11(1):56-63, 1999.
• Ying Liu, Dengsheng Zhang, Guojun Lu, and Wei-Ying Ma. A Survey of Content-Based Image Retrieval with High-Level Semantics. Pattern Recognition, 40(1):262-282, 2007.

4. Grading

Topics under theme #1 will be presented on February 25. Topics under theme #2 will be presented on March 24. The components of the grade for this project are as follows (a total of 10 points):

• 4 points: Lecture content — demonstrated understanding of the topic.
• 3 points: Lecture presentation — effectiveness of the delivery.
• 3 points: Discussion — effectiveness in leading the class discussion.

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This page, first created: 14 Jan 2008; last updated: