By reviewing the previous ontology-based approaches, we summarize the following three purposes that ontologies have been introduced to semantic data mining: These include an ontology of core data mining entities (OntoDM-core), an ontology of datatypes (OntoDT), and ontology for representing the knowledge discovery process (OntoDM-KDD). data (e.g., taxonomy of datasets, data mining tasks, and data mining algorithms). The data mining phase which includes data preparation, selection, and extraction of knowledge. Data warehousing approach supported by ontology will be described for effective data mining. The Ontology building from data mining can be formulated into two phases, the data mining phase and the ontology building phase as explained below[3][4]. We populated a segment from OntoDM-core with instances, performed reasoning, and queried the ontology. In the context of the development of the ontology of data mining that needs to be general enough to allow the representation of mining structured data, we developed a separate ontology module, named OntoDT, for representing the knowledge about datatypes. The ontology comprises of three modular sub-ontologies, covering different aspects of data mining and knowledge discovery process. The ontology includes a definition of constraints, a taxonomy of constraints, constraint-based data mining (CBDM) tasks, scenarios and workflows. Vaccine safety is a concerning problem of the public, and many signal detecting methods have been developed to identify relative risks between vaccines and adverse events (AEs). Recently, Ontologies have come into picture, as being an integral part of The results often turn out to be inaccurate and lack of clinical meaning. Keywords: data mining, ontology engineering, linked data, ontologies 1 Introduction Due to the rapid growth of the open data and linked data [1] movements, more and more data are being made available and directly accessible from a wide range of do-mains, areas and organisations. This is usually referred to as the Knowledge discovery process in the context of databases. Those methods usually focus on individual AEs, where the randomness of data is high. Ontology based data warehousing framework with fine-grained multidimensional data structures facilitates mining and visualization of data patterns, trends, and correlations hidden under massive volumes of data. Data Mining is the process of extracting potentially useful knowledge from raw data. This paper proposes using the Apriori algorithm of association rules, and clustering analysis based on an ontology-based data mining approach, for mining customer knowledge from the database. 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