Various emerging applications using IoT require frequent ontology update and enrichment in ways to keep track of the frequent changes in the knowledge. Most of the previous ontology enrichment methods use string data in the web to enrich ontology. These methods could be efficient for extracting new instances, but they have difficulties extracting relations between instances. Also, there are less useful data in web documents when enriching ontology of technical domain. In order to solve these problems, we propose a hybrid approach for a new semi-automated ontology enrichment system which expands the present ontology by using both linked data extracted from other ontology of the same (or similar) application domain and string data crawled from the web. Our system enriches an ontology with new instances of concepts and relations in two phases. First, it extracts new instances and relations from a reference ontology of the same or similar domain. Second, it computes confidence value to check validity of the possible relations between the original instances and the new ones using crawled data from the web search and add them to the present ontology. Our experiment demonstrates that the proposed two-phase hybrid approach achieves improved efficiency and accuracy for enriching ontology instances.