Semantic approach is a completely new way of understanding, analysing and delivering electronic content available on the World Wide Web making use among others of semantic network. SemanTech SA is a {tag semantic} web-based solution developments company that intents to provide a user friendly {tag knowledge management} system {tag semantic-based}. Two devices (OntoGene™ Ontology Generator and i2Me™ Delivery Engine) have already been designed with the participation of leading {tag semantic} application experts and cognitive science experts.

Enterprises possess thousands of different data systems, each with its own vocabulary. This fragmentation results in poor information quality, an inflexible environment and high IT costs. A key imperative for IT is to manage the enterprise’s data and to elevate it into a cohesive body of information. Existing solutions are limited, being either tactical (applying to one database at a time) or passive (not actively helping the integration and quality of data). The {tag semantic} approach captures the agreed-upon business meaning of each data source and elevates the data into unambiguous business information. Core elements of a Semantic Information Architecture are a catalog of data assets (metadata), an agreed upon business vocabulary (Information Model) and the formal capture of data’s business meaning by reference to the Information Model ({tag semantics}). A Semantic Information Architecture provides automated support for data management, data integration and data quality. Value is created by driving a higher quality of business information, providing business agility and lowering IT costs. Semantic Information Architecture should be applied first to specific pain.

Before data assets can be understood, they must be cataloged. Metadata should include the asset’s schema as well as information about an asset’s location, usage, origin, relationship to other assets, rules associated with it, and assignment of ownership and responsibility. Some of this metadata may be scanned automatically from assets such as relational databases or from existing sources of metadata.

The Information Model is a rich central model of the business. A traditional data model may serve as the basis for an Information Model, but data models should be extended to a full ontology. An ontology literally means a study of what exists or as Gartner put it: “The objective of an ontology is to provide a formal specification of part of the real world.” An ontological Information Model is therefore typically richer than a data model in its view of the business, including different levels of generalization/specialization and a layer of business rules, in addition to the traditional entities and relationships. This richness allows the Information Model to serve as an authoritative reference by which meaning is given to multiple data assets, regardless of format or technology. 3 Semantics captures the formal meaning of data. It is achieved by mapping (or rationalizing) the data’s schema to the Information Model. Any database or message format with a schema can be mapped, including relational databases, XML, older hierarchical databases, network databases and COBOL Copybooks. Data that is structured without a schema (e.g. EDI messages or flat files) can be parsed and then mapped. Software can aid the mapping process using type information, foreign keys and even name similarities to suggest matches and to provide an efficient graphical environment. However, mapping will never be totally automatic; only a database administrator or other expert will know how to interpret data accurately. Semantic mapping creates immediate savings. Having mapped an asset once to an Information Model, its relationship to all other assets may be inferred automatically. Every asset is therefore mapped only once, in contrast to the current situation in which every data asset is mapped many times, often using inappropriate and non-scalable tools such as MS Word or Excel.