Semantic SPARQL similarity search over RDF knowledge graphs

Abstract

RDF knowledge graphs have attracted increasing attentions these years. However, due to the schema-free nature of RDF data, it is very difficult for users to have full knowledge of the underlying schema. Furthermore, the same kind of information can be represented in diverse graph fragments. Hence, it is a huge challenge to formulate complex SPARQL expressions by taking the union of all possible structures. In this paper, we propose an effective framework to access the RDF repository even if users have no full knowledge of the underlying schema. Specifically, given a SPARQL query, the system could return as more answers that match the query based on the semantic similarity as possible. Interestingly, we propose a systematic method to mine diverse semantically equivalent structure patterns. More importantly, incorporating both structural and semantic similarities we are the first to propose a novel similarity measure, semantic graph edit distance . In order to improve the efficiency performance, we apply the semantic summary graph to summarize the knowledge graph, which supports both high-level pruning and drill-down pruning. We also devise an effective lower bound based on the TA-style access to each of the candidate sets. Extensive experiments over real datasets confirm the effectiveness and efficiency of our approach.

Semantic locality‐based approximate knowledge graph query

“…Afterwards, we present an approximate query processing method based on the semantics obtained above to return top‐k results that satisfy the user's query intention. Moreover, we conduct experiments on the dataset of the work of Zheng et al to demonstrate the effectiveness and efficiency of our solution.…”

“…Besides GraB, S4 is another graph query method that takes the graph edit distance to measure the graph semantics. S4 requires some prior knowledge as input to mine the semantic patterns with equivalent meaning.…”

“…Structure similarity based graph pattern match is one commonly used technology to support query over knowledge graph . Zheng et al proposed a instance‐driven mining algorithm to detect diverse structure patterns with equivalent semantic meanings of priori knowledge . On the other hand, graph edit distance is adopted in existing approaches .…”

“…The example of ''schema-free'' RDF KG and an example of simple SPARQL expression FIGURE 2 The basic idea of trans-based semantic vector embedding Figure 1. The semantic similarity-based approaches 2,7,[15][16][17] use the concept-level or string edit distance to denote semantics without considering the real knowledge semantics. Moreover, other works 2,18,19 assisted non-expert users by taking prior knowledge to discover more similar answers.…”

“…Large scale structured data has been extracted and produced in today's fast‐paced data‐driven businesses environment, which increases the demand for managing and processing such big data as graph pattern, especially the knowledge graph (KG) schema effectively and efficiently . RDF (Resource Description Framework) data that consists of a set of triples <subject, predicate, object> is one common solution to model big data as KG schema, in where the subjects and objects (as known as labeled entities) can be viewed as vertices and predicate is the labeled edges.…”