Horizontal ontologies are general in nature, such as space-time relationships. These are common ontologies that span multiple domains, are not applicable to any single vertical space and provide a mechanism to organize and standardize information content. We've employed this type of ontology for years in the form of object models, hierarchies, taxonomies and, in many cases, XML vocabularies.
Vertical ontologies, which also incorporate features from horizontal ontologies, are domain-specific, such as natural languages for healthcare or financial services. Vertical ontologies not only define data in terms of semantics native to a particular vertical industry, they also contain rules and formal computer languages that can perform certain types of runtime automated reasoning. This means we understand the meta data and have logic bound to the meta data as well.
The use of vertical ontologies, which extend the capabilities of horizontal applications, is where the most value exists. As we learn to define these ontologies as common frameworks for specific business requirements and define the reuse of such frameworks applicable across multiple like-domains, we also learn to apply languages and reasoning techniques. Ultimately, this provides repeatable information formats, rules and logic that, in turn, provide data integration architects with the ability to leverage existing solutions rather than form them from general-purpose middleware and application development technology.
Web-Based Standards and Ontologies
The use of languages for ontology is beginning to appear, all built on reasoning techniques that provide for the development of special-purpose reasoning services. In fact, the W3C is creating a Web standard for ontology language as part of its effort to define semantic standards for the Web. The Semantic Web is the abstract representation of data on the World Wide Web, based on the resource description framework (RDF) standards (see sidebar) and other standards still to be defined. It is being developed by the W3C, in collaboration with a large number of researchers and industrial partners.
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Ontology and Mapping Servers How do you implement ontologies in your data integration problem domain? In essence, some technology - an integration broker or applications server, for instance - needs to act as an ontology server and/or mapping server. An ontology server houses the ontologies that are created to service the data integration problem domain.4 There are thee types of ontologies stored: shared, resource and application ontologies. Shared ontologies are made up of definitions of general terms that are common across and between enterprises. Resource ontologies are made up of definitions of terms used by a specific resource. Application ontologies are native to particular applications, such as an inventory application. Mapping servers store the mappings between ontologies (stored in the ontology server). The mapping server also stores conversion functions, which account for the differences between schemas native to remote source and target systems. Mappings are specified using a declarative syntax that provides reuse. |
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RDF and Ontologies RDF (resource description framework), a part of the XML story, provides interoperability between applications that exchange information. RDF is another Web standard that's finding use everywhere, including data integration. RDF was developed by the W3C to provide a foundation of meta data interoperability across different resource description communities, and is the basis for the W3C movement to ontologies. RDF uses XML to define a foundation for processing meta data and to provide a standard meta data infrastructure for both the Web and the enterprise. The difference between the two is that XML is used to transport data using a common format, while RDF layers on top of XML defining a broad category of data. When the XML data is in the RDF format, applications are then able to understand the data without understanding who sent it. RDF extends the XML model and syntax to be specified for describing either resources or a collection of information. (XML points to a resource in order to scope and uniquely identify a set of properties known as the schema.) RDF meta data can be applied to many areas, including data integration. RDF is also able to support new technology (such as intelligent software agents and exchange of content rating). RDF itself does not offer predefined vocabularies for authoring meta data. However, the W3C does expect standard vocabularies to emerge once the infrastructure for meta data interoperability is in place. Anyone, or any industry, can design and implement a new vocabulary. The only requirement is that all resources be included in the meta data instances using the new vocabulary. RDF benefits data integration in that it supports the concept of a common meta data layer that is shareable throughout an enterprise or between enterprises. Thus, RDF can be used as a common mechanism for describing data within the data integration problem domain. |
In order for the Semantic Web to function, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. This notion is known as knowledge representation. To this end, and in the domain of the World Wide Web, computers will find the meaning of semantic data by following hyperlinks to definitions of key terms and rules for reasoning about data logically. The resulting infrastructure will spur the development of automated Web services such as highly functional agents.1 What's important here is that the work now being driven by the W3C as a way to manage semantics on the Web is applicable, at least at the component level, to the world of data integration, much like XML and Web services.
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