Keyword Search on RDF Graphs - A Query Graph Assembly Approach

Abstract

Keyword search provides ordinary users an easy-to-use interface for querying RDF data. Given the input keywords, in this paper, we study how to assemble a query graph that is to represent user's query intention accurately and efficiently. Based on the input keywords, we first obtain the elementary query graph building blocks, such as entity/class vertices and predicate edges. Then, we formally define the query graph assembly (QGA) problem. Unfortunately, we prove theoretically that QGA is a NP-complete problem. In order to solve that, we design some heuristic lower bounds and propose a bipartite graph matching-based best-first search algorithm. The algorithm's time complexity is O(k 2l · l 3l ), where l is the number of the keywords and k is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword. Although QGA is intractable, both l and k are small in practice. Furthermore, the algorithm's time complexity does not depend on the RDF graph size, which guarantees the good scalability of our system in large RDF graphs. Experiments on DBpedia and Freebase confirm the superiority of our system on both effectiveness and efficiency.

Structured query construction via knowledge graph embedding

“…Translation-based models capture the structural information of KGs precisely, and they have been adopted in tasks such as KG completion [23] and graph-structured query construction [15]. However, translation-based models ignore contextual information and are not suitable for tasks such as classification and regression [29].…”

“…In this section, we discuss related researches on the graph-structured query construction problem. Recently, a variety of techniques, including semantic parsing [49,19,3,14,24], templates [2,34,47,34], the interaction with users [48,25], and KG embeddings [5,6,15,44], have been leveraged by query construction and question answering models.…”

Semantic locality‐based approximate knowledge graph query

“…However, such prediction is limited since it can only return one‐hop results without more diversified query results. To make knowledge graph embedding more scalable to the real‐world queries, a query assembly method is proposed based on Trans‐E to support keyword search in knowledge graph …”

“…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.…”

“…To make knowledge graph embedding more scalable to the real-world queries, a query assembly method is proposed based on Trans-E to support keyword search in knowledge graph. 7 In this paper, we propose a semantic approximate query method by considering the real knowledge semantics to return top-k desired results.…”