As an increasing amount of the knowledge graph is published as Linked Open Data, semantic entity search is required to develop new applications. However, the use of structured query languages such as SPARQL is challenging for non-skilled users who need to master the query language as well as acquiring knowledge of the underlying ontology of Linked Data knowledge bases. In this article, we propose the Sematch framework for entity search in the knowledge graph that combines natural language query processing, entity linking, entity type linking and semantic similarity based query expansion. The system has been validated in a dataset and a prototype has been developed that translates natural language queries into SPARQL.