Data Storage Management in Semantic Web Service

Semantic Web design followed three major principles: all the resources can be used URI (Universal Resource Indicator) to identify; resources and links can have types; part, fragment, incomplete information is permitted. Semantic Web is given in the relationship between the level of structure, it is mainly based on XML and RDF/RDFS-based and Ontology Construction in the above rules and logic to complete the semantic-based knowledge representation and reasoning in order to for computers to understand and deal with. The set of standards to be provided support rely on the RDF Core Working Group and the Web Ontology Working Group as shown in Fig 1 below. Figure 1. Semantic Web Hierarchy Architecture Data organization in files on the operating system level is not sufficient to fit the requirements of access performance and easy to use data extraction. The relational model presents the database designer with a great degree of flexibility; it captures little meaning of the stored data and offers limited data integrity capabilities. These technologies are actually quite limited in scope: Web data lacks machine-understandable semantics, so it is generally not possible to automatically extract concepts or relationships from this data or to relate items from different sources. The Web community is attempting to address this limitation by designing a Semantic Web [1]. The Semantic Web aims to provide data in a format that embeds semantic information, and then seeks to develop sophisticated query tools to interpret and combine this information [2]. OWL-S [3] is ontology to support greater automation in service selection and invocation, automated translation of message content between heterogeneous interoperating services, and service composition [4]. Problems in data access may be caused by large amounts of data as for the semantic Web service. Data organization in files on the operating system level is not sufficient to fit the requirements of access performance and easy to use data extraction. Semantic storage management is suggested as an alternative. It is aimed to overcome the limitation of traditional relational storage system by including more information about the meaning of the data and what they represent in the real world. Service Discovery Search Model allows for more complex data relationships while maintaining data integrity as well as the flexibility to model the schema in both a hierarchical or relational fashion. Data access and storage are formulated within the semantics of the data model neglecting the details of physical storage. In this paper, we study the applicability of semantically technology underneath database management model. The Service Discovery Search Model as shown in Fig 2, and has given provides access to a multitude of data sources through a single mediated schema. The Service Discovery Search Model is a collection of cooperating database systems that are autonomous and possibly heterogeneous. In this paper, we define reference architecture for distributed database management systems from system and schema viewpoints and show how various the model architectures can be developed. We describe how semantic storage management overcomes some of the weaknesses of the other existing database systems. The Service Discovery Search Model offers a language for mediating between data bases on the Semantic Web, which maps both the domain structure and