LaSaS: an Aggregated Search based Graph Matching Approach

Graph querying is crucial to fully exploit the knowledge within the widely used graph datasets. However, graph datasets are usually noisy which makes the approximate graph matching tools favored to overcome restrictive query answering. In this paper, we introduce a new framework of approximate graph matching based on aggregated search called Label and Structure Similarity Aggregated Search (LaSaS). LaSaS enables effective and efficient graph querying without considering any fixed schema of the data graph by (i) using the aggregated search strategy to increase the number of answers, (ii) using a lightweight graph similarity metric that takes into account nodes label and graph structure similarity to enable finding approximate matches and also by (iii) using a simple graph weight update routine instead of computing the maximum common subgraph which reduces the overall computation cost. We evaluated our proposed approach over the real-life DBpedia graph and results show the effectiveness and stability of the approach on different parameter settings. Moreover, results also show that LaSaS yields more precise matches in a shorter amount of time when compared to state-of-the-art related approaches. Keywords—Graph querying, Graph matching, Graph similarity metric, Aggregated search.