In recent years, deep learning has achieved great success in many computer vision
applications. Convolutional neural networks (CNNs) have lately emerged as a major …

Query processing over graph-structured data is enjoying a growing number of applications.
A top-k keyword search query on a graph finds the top k answers according to some ranking …

Conventional keyword search engines are restricted to a given data model and cannot
easily adapt to unstructured, semi-structured or structured data. In this paper, we propose an …

Keyword search enables web users to easily access XML data without the need to learn a
structured query language and to study possibly complex data schemas. Existing work has …

Uncertainty in data naturally arises in various applications, such as data integration and
Web information extraction. Probabilistic XML is one of the concepts that have been …

Empowering users to access databases using simple keywords can relieve the users from
the steep learning curve of mastering a structured query language and understanding …

In this paper, we study the problem of effective keyword search over XML documents. We
begin by introducing the notion of Valuable Lowest Common Ancestor (VLCA) to accurately …

Large knowledge bases consisting of entities and relationships between them have become
vital sources of information for many applications. Most of these knowledge bases adopt the …

Inspired by the great success of information retrieval (IR) style keyword search on the Web,
keyword search on XML has emerged recently. The difference between text database and …

Keyword search for smallest lowest common ancestors (SLCAs) in XML data has recently
been proposed as a meaningful way to identify interesting data nodes inXML data where …