Graph keyword search is the process of extracting small subgraphs that contain a set of query keywords from a graph. This problem is challenging because there are many constraints, including distance constraint, keyword constraint, search time constraint, index size constraint, and memory constraint, while the size of data is inflating at a very high speed nowadays. Existing greedy algorithms guarantee good performance by sacrificing the accuracy to generate approximate answers, and exact algorithms promise exact answers but require a high memory consumption for loading indices and advanced knowledge about the maximum distance constraint. For big data applications, existing techniques are inefficient and impractical due to huge memory consumption and varied distance constraint. We propose a new keyword search algorithm that finds exact answers with low memory consumption and without advanced knowledge of maximum distance constraint. This algorithm builds a compact index structure offline based on a recent labeling index for shortest path queries. At the query time, it finds the answer efficiently by examining a small portion of the index related to a query.
['Yue Wang', 'Ke Wang', 'Ada Wai Chee Fu', 'Raymond Chi Wing Wong']
KeyLabel algorithms for keyword search in large graphs