PhD Thesis Proposal Defence

"Adaptive XML Searching and Ranking"

by

Mr. Ho-Lam LAU


Abstract:

The evolution of computing technology suggests that it has become
more feasible to offer access to Web information in a ubiquitous
way, through various kinds of interaction devices such as PCs,
laptops, palmtops, and so on. As XML has become a de-facto
standard for exchanging Web data, an interesting and practical
research problem is the development of models and techniques to
satisfy various needs and preferences in searching XML data.

In this proposal, we employ a list of simple XML tagged keywords
as a vehicle for searching XML fragments in a collection of XML
documents. In order to deal with the diversified nature of XML
documents as well as user preferences, we propose a novel
Multi-Ranker Model (MRM), which is able to abstract a spectrum of
important XML properties and adapt the features to different XML
search needs.

The MRM is composed of three ranking levels. The lowest level
consists of two categories of similarity and granularity features.
At the intermediate level, we define four tailored XML Rankers
(XRs), which consists of different lower level features and have
different strength in searching XML fragments. The XRs are trained
via a learning mechanism called the Ranking Support Vector Machine
in a voting Spy Na\"ive Bayes Framework (RSSF). The RSSF takes as
input a set of labeled fragments and feature vectors and generates
as output Adaptive Rankers (ARs) in the learning process. The ARs
are defined over the XRs and generated at the top level of the
MRM.

We show empirically that the RSSF is able to improve the MRM
significantly  in the learning process that needs only a small set
of training XML fragments. We demonstrate that the trained MRM is
able to bring out the strengths of the XRs in order to adapt
different preferences and queries.


Date:     		Friday, 29 June 2007

Time:                   2:00p.m.-4:00p.m.

Venue:                  Room 3501
			lifts 25-26

Committee Members:      Dr. Wilfred Ng (Supervisor)
                        Dr. James Kwok (Chairperson)
			Prof. Frederick Lochovsky
			Dr. Qiong Luo


**** ALL are Welcome ****