Keyword search on external memory data graphs

Abstract

Keyword search on graph structured data has attracted a lot of attention in recent years. Graphs are a natural "lowest common denominator" representation which can combine relational, XML and HTML data. Responses to keyword queries are usually modeled as trees that connect nodes matching the keywords.In this paper we address the problem of keyword search on graphs that may be significantly larger than memory. We propose a graph representation technique that combines a condensed version of the graph (the "supernode graph") which is always memory resident, along with whatever parts of the detailed graph are in a cache, to form a multi-granular graph representation. We propose two alternative approaches which extend existing search algorithms to exploit multigranular graphs; both approaches attempt to minimize IO by directing search towards areas of the graph that are likely to give good results. We compare our algorithms with a virtual memory approach on several real data sets. Our experimental results show significant benefits in terms of reduction in IO due to our algorithms.

Top-k Keyword Search Over Graphs Based On Backward Search

“…[13] [14] [2] [15] [16] are well known methods about keyword search over graphs. With the development of Semantic Web, a large amount of RDF data is distributed to the Internet.…”

Keyword Search over Graph-structured Data for Finding Effective and Non-redundant Answers

“…As the amount of data increases rapidly, an efficient and effective query system is much needed. Keyword search has been attracting a lot of attention since it allows users to express their information need using simple keywords [1][2][3][4][5][6][7][8][9][10].…”

“…There have been proposed several approaches based on the distinct root semantics, where the relevance of a sub-tree is computed as a function of the shortest paths from the root to the nodes containing query keywords. For each node in the graph, they choose at most one sub-tree rooted at the node as a candidate answer to the query [2,3,5,10]. By reducing the number of candidates significantly, they can process top-k query over a large volume of data more efficiently than other approaches.…”

“…2, for instance, when a query q = {k 1 , k 2 , k 3 } is given on G 2 , the set of top-3 answers to the query based on the distinct root semantics is {T 4 , T 5 , T 6 }. Note that these sub-trees have different root nodes but share the same set of content nodes {v 5 , v 6 }. If we select T' 5 and T' 6 instead of T 5 and T 6 , respectively, into top-3 answers, we can obtain results which are duplication-free and diverse in terms of the content nodes.…”

“…Note that these sub-trees have different root nodes but share the same set of content nodes {v 5 , v 6 }. If we select T' 5 and T' 6 instead of T 5 and T 6 , respectively, into top-3 answers, we can obtain results which are duplication-free and diverse in terms of the content nodes.…”