Context-based Diversification for Keyword Queries over XML Data

TL;DR

This paper introduces a novel approach to diversify XML keyword search results by analyzing different contexts within the data, improving relevance and coverage for vague queries.

Contribution

It proposes a new XML keyword search diversification model and three algorithms to effectively generate and evaluate diversified query candidates.

Findings

The diversification model significantly improves search relevance.

Algorithms efficiently evaluate multiple query candidates.

Experimental results validate the effectiveness and efficiency of the approach.

Abstract

While keyword query empowers ordinary users to search vast amount of data, the ambiguity of keyword query makes it difficult to effectively answer keyword queries, especially for short and vague keyword queries. To address this challenging problem, in this paper we propose an approach that automatically diversifies XML keyword search based on its different contexts in the XML data. Given a short and vague keyword query and XML data to be searched, we firstly derive keyword search candidates of the query by a classifical feature selection model. And then, we design an effective XML keyword search diversification model to measure the quality of each candidate. After that, three efficient algorithms are proposed to evaluate the possible generated query candidates representing the diversified search intentions, from which we can find and return top-$k$ qualified query candidates that are most relevant to the given keyword query while they can cover maximal number of distinct results.At last, a comprehensive evaluation on real and synthetic datasets demonstrates the effectiveness of our proposed diversification model and the efficiency of our algorithms.