Keyword Queries over the Deep Web

2016-11-14

International Conference on Conceptual Modeling

AI Summary

To address the challenge of keyword search over structured deep web data—particularly restricted tables—that are inherently inaccessible to conventional keyword-based retrieval, this paper proposes a keyword query modeling framework tailored for the deep web. The method comprises three key components: (1) schema-agnostic virtual document generation, which maps invisible database contents into indexable textual representations; (2) cross-table semantic matching integrated with query rewriting to enhance semantic alignment between keywords and underlying data; and (3) joint optimization of result ranking via table structure inference, query expansion, and learning-to-rank techniques. Experiments on real-world deep web datasets demonstrate substantial improvements: NDCG@10 increases by 32% on average over baseline methods, with concurrent gains in both recall and precision. This work establishes the first end-to-end, systematic modeling paradigm for deep web keyword search, advancing the discoverability of deep web data.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Semantic Web
• Natural Language Processing: Information Extraction

Application Category

• Search and Retrieval-Augmented AI: Web search models and ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Problem

• Deep Web
• Search Engine Optimization
• Data Retrieval

Innovation

• Deep Web Search
• Dynamic Page Information Retrieval
• Optimized Search Strategy