Semantic similarity method for keyword query system on RDF

Abstract

Keyword query on RDF data is an effective option because it is lightweight and it is not necessary to have prior knowledge on the data schema or a formal query language such as SPARQL. However, optimizing the query processing to produce the most relevant results with only minimum computations is a challenging research issue. Current proposals suffer from several drawbacks, e.g., limited scalability, tight coupling with the existing ontology, and too many computations. To address these problems, we propose a novel approach to keyword search with automatic depth decisions using the relational and semantic similarities. Our approach uses a predicate that represents the semantic relationship between the subject and object. We take advantage of this to narrow down the target RDF data. The semantic similarity score is then calculated for objects with the same predicate. We make a linear combination of two scores to get the similarity score that is used to determine the depth of given keyword query results. We evaluate our algorithm with other approaches in terms of accuracy and query processing performance. The results of our empirical experiments show that our approach outperforms other existing approaches in terms of efficiency and query processing performance.