ELCA Evaluation for Keyword Search on Probabilistic XML Data

TL;DR

This paper introduces a novel method to evaluate ELCA keyword search results on probabilistic XML data, providing an efficient algorithm that improves result relevance and computational performance.

Contribution

It defines probabilistic ELCA semantics, proposes a world-independent probability computation approach, and develops an efficient algorithm for probabilistic ELCA result retrieval.

Findings

The proposed algorithm effectively finds all probabilistic ELCA results.

It outperforms SLCA in result relevance and efficiency.

Experimental results demonstrate scalability and improved performance.

Abstract

As probabilistic data management is becoming one of the main research focuses and keyword search is turning into a more popular query means, it is natural to think how to support keyword queries on probabilistic XML data. With regards to keyword query on deterministic XML documents, ELCA (Exclusive Lowest Common Ancestor) semantics allows more relevant fragments rooted at the ELCAs to appear as results and is more popular compared with other keyword query result semantics (such as SLCAs). In this paper, we investigate how to evaluate ELCA results for keyword queries on probabilistic XML documents. After defining probabilistic ELCA semantics in terms of possible world semantics, we propose an approach to compute ELCA probabilities without generating possible worlds. Then we develop an efficient stack-based algorithm that can find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA algorithm and compare it with its SLCA counterpart in aspects of result effectiveness, time and space efficiency, and scalability.