Enhancing Content-And-Structure Information Retrieval using a Native XML Database
Jovan Pehcevski, James A. Thom, Anne-Marie Vercoustre
Abstract
Three approaches to content-and-structure XML retrieval are analysed in this paper: first by using Zettair, a full-text information retrieval system; second by using eXist, a native XML database, and third by using a hybrid XML retrieval system that uses eXist to produce the final answers from likely relevant articles retrieved by Zettair. INEX 2003 content-and-structure topics can be classified in two categories: the first retrieving full articles as final answers, and the second retrieving more specific elements within articles as final answers. We show that for both topic categories our initial hybrid system improves the retrieval effectiveness of a native XML database. For ranking the final answer elements, we propose and evaluate a novel retrieval model that utilises the structural relationships between the answer elements of a native XML database and retrieves Coherent Retrieval Elements. The final results of our experiments show that when the XML retrieval task focusses on highly relevant elements our hybrid XML retrieval system with the Coherent Retrieval Elements module is 1.8 times more effective than Zettair and 3 times more effective than eXist, and yields an effective content-and-structure XML retrieval.
Create a lesson
Related papers
WISE: An Agentic Framework for Whole-page Multimodal Search Experience Attribution
Wenyi Xu, Renzhe Zhou, Songyang Li et al.
Deliberate Latent Reasoning for Sequential Recommendation
Haobo Zhang, Qiannan Zhu, Yutao Zhu et al.
PMRecBench: A Benchmark for Personalization in Recommendation Agents under Multimodal Queries
Xiaoyu Zhang, Yougang Lyu, Bin Wu et al.
Generative Retrieval under Real-Time Supply Constraints in Local Commerce
Shaopeng Liang, Biao Zhang, Hongzhen Ye et al.
Do Search-Augmented Recommenders Use the Latest Search Episode?
Jiandong Ding
One Basket, Many Experts: Decorrelated Re-ranking for Financial Asset Recommendation
Ryuki Unno, Keigo Sakurai, Koshi Watanabe et al.