Effective keyword query processing with an extended answer structure in large graph databases

Abstract

Effective keyword query processing with an extended answer structure in large graph databases Chang-Sup Park Sungchae Lim Article information:To cite this document: Chang-Sup Park Sungchae Lim , (2014),"Effective keyword query processing with an extended answer structure in large graph databases"If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The paper aims to propose an effective method to process keyword-based queries over graph-structured databases which are widely used in various applications such as XML, semantic web, and social network services. To satisfy users' information need, it proposes an extended answer structure for keyword queries, inverted list indexes on keywords and nodes, and query processing algorithms exploiting the inverted lists. The study aims to provide more effective and relevant answers to a given query than the previous approaches in an efficient way. Design/methodology/approach -A new relevance measure for nodes to a given keyword query is defined in the paper and according to the relevance metric, a new answer tree structure is proposed which has no constraint on the number of keyword nodes chosen for each query keyword. For efficient query processing, an inverted list-style index is suggested which pre-computes connectivity and relevance information on the nodes in the graph. Then, a query processing algorithm based on the pre-constructed inverted lists is designed, which aggregates list entries for each graph node relevant to given keywords and identifies top-k root nodes of answer trees most relevant to the given query. The basic search method is also enhanced by using extend inverted lists which store additional relevance information of the related entries in the lists in order to estimate the relevance score of a node more closely and to find top-k answers more efficiently. Findings -Experiments with real datasets and various test queries were conducted for evaluating effectiveness and performance of the proposed methods in comparison with one of the previous approaches. The experimental results show that the proposed methods with an extended answer structure produce more effective top-k results than the compared previous method for most of the queries, especially for those with O...

Efficient keyword search on graph data for finding diverse and relevant answers

“…The former semantics aims to find the most relevant Stainer subtrees that contain the input keywords and have the smallest total weight on the edges (Bhalotia et al , 2002; Ding et al , 2007; Golenberg et al , 2008; Kimelfeld and Sagiv, 2006; Liu et al , 2016). The latter semantics restricts the root nodes of the selected subtrees to be distinct and applies a different cost function to the subtrees which measures the total weight of the shortest paths from the root to the content nodes (Dalvi et al , 2008; He et al , 2007; Kacholia et al , 2005; Le, et al , 2014; Park, 2018a; Park and Lim, 2016). On the other hand, there have been proposed approaches to finding various types of subgraphs as desired answers to the query, such as r -radius Steiner subgraphs (Li et al , 2008), multicenter communities (Qin et al , 2009), r -cliques (Kargar and An, 2011), cohesive subgraphs (Zhu et al , 2018) and strongly connected subgraphs (Bryson et al , 2020).…”

“…Liu et al (2016) proposed an approach to finding a set of minimal and non-overlapping subtrees that do not share even internal nodes and edges, as well as content nodes; hence, the search results are too exclusive and limited. Park (2018a) presented a top- k keyword search algorithm to find subtrees that do not share any content node and have the highest query relevance under the distinct root semantics. Park (2018b) suggested a keyword search approach that relaxes the distinct root semantics to allow a limited degree of redundancy for the root nodes of the answer subtrees to obtain more diverse search results.…”

“…In the literature of keyword search on graph data, various relevance scoring functions have been proposed for the answers to a query, which usually consider textual relevance of the content nodes for the input keywords and the compactness of the answer structure measured by the proximity between the content nodes (Yang et al , 2021). In this study, we adopt a relevance scoring function for answer trees proposed by Park (2018a), while other relevance measures can be used with our approach as well. In the rest of the paper, the relevance of answer tree T with respect to a given query q is denoted by rel ( T , q ).…”