Saravadee Sae Tan

Learn More
Having broad coverage of search results returned by various search sources, combining and organizing these results in a meaningful way has become a common issue in the field of information retrieval. In this demo paper, we describe our meta search system, MICE, that is able to aggregate and classify search results based on user-customized categories.(More)
This paper addresses the problem of matching between highly heterogeneous structures. The problem is modeled as a classification task where training examples are used to learn the matching between structures. In our approach, training is performed using partially labeled data. We propose a Greedy Mapping approach to generate training examples from partially(More)
Structured retrieval aims at exploiting the structural information of documents when searching for documents. Structured retrieval makes use of both content and structure of documents to improve information retrieval. Therefore, the availability of semantic structure in the documents is an important factor for the success of structured retrieval. However,(More)
On the web, most structured document collections consist of documents from different sources and marked up with different types of structures. The diversity of structures has led to the emergence of heterogeneous structured documents. The heterogeneity of structured documents is one of the reason for query-document mismatch in structured document retrieval.(More)
Resembling the desktop of personal computer in which files and applications are stored, MICE<sup>3</sup> desktop is a Web-based information desktop where individual can keep and manage resources such as links to Web sites and services, as well as Web sources and documents. Resources on the desktop are represented in Web directory structure and annotated(More)
Categories are used to organize information and knowledge in directory system, folder etc. As the amount of information increase and the types of information diversify, it is common to have more categories created. As the number of categories increases, it becomes more difficult to organize, manage and look up information from existing categories. In this(More)