@inproceedings{8c9f1473e09548beacc0400c73955d2a,
title = "Leveraging Pattern Mining Techniques for Efficient Keyword Search on Data Graphs",
abstract = "Graphs model complex relationships among objects in a variety of web applications. Keyword search is a promising method for extraction of data from data graphs and exploration. However, keyword search faces the so called performance scalability problem which hinders its widespread use on data graphs. In this paper, we address the performance scalability problem by leveraging techniques developed for graph pattern mining. We focus on avoiding the generation of redundant intermediate results when the keyword queries are evaluated. We define a canonical form for the isomorphic representations of the intermediate results and we show how it can be checked incrementally and efficiently. We devise rules that prune the search space without sacrificing completeness and we integrate them in a query evaluation algorithm. Our experimental results show that our approach outperforms previous ones by orders of magnitude and displays smooth scalability.",
keywords = "Canonical form, Graph data, Keyword search, Tree encoding",
author = "Xinge Lu and Dimitri Theodoratos and Aggeliki Dimitriou",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd 2020.; 20th International Conference on Web Information Systems Engineering, WISE 2019 and on the International Workshop on Web Information Systems in the Era of AI, 2019 ; Conference date: 19-01-2020 Through 22-01-2020",
year = "2020",
doi = "10.1007/978-981-15-3281-8_10",
language = "English (US)",
isbn = "9789811532801",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "98--114",
editor = "U, {Leong Hou} and Jian Yang and Yi Cai and Kamalakar Karlapalem and An Liu and Xin Huang",
booktitle = "Web Information Systems Engineering - WISE 2019 Workshop, Demo, and Tutorial, Revised Selected Papers",
}