Semantic Integration

Information Management Magazine, June 2008

Stephen Lahanas

What do service-oriented architecture (SOA), databases, program logic, business process and program management have in common? All of these elements within a single enterprise should share the same set of terminology and conventions. There ought to be a unifying foundation that logically binds all of these pieces together into one common picture. However, this is seldom the case, and that fact is one of the primary reasons why systems integration has traditionally been so costly, time-consuming and ultimately unsuccessful. There is, though, a practice area emerging specifically to deal with what may likely be the most pivotal of all enterprise technologies – this new practice is semantic integration. The goal of this article is to provide a high level overview of semantic integration as a community of practice and to explore the practical implications and impacts associated with it in the context of a variety of typical IT projects or issues.

Technology alone does not solve problems. People solve problems by leveraging the appropriate technologies within the context of communities of practice. Those communities of practice involve the utilization of one or more methodologies that have been tailored to suit the needs of the community, the problem space and the related technologies involved. It is important to keep this in mind because most of the expectation disconnects that occur around IT projects tend to be based upon the promise or potential of a particular technology before the appropriate community of practice has been established.

This situation is more commonly referred to as the “hype cycle.” We are familiar with the hype cycle surrounding SOA and somewhat familiar with the related term, “semantic Web.” In both cases, the promise of those technologies surpassed the maturity of the communities of practice that arose around them, and the first generation of implementations suffered greatly. Another thing to keep in mind is that to solve an enterprise technical issue, one often needs to exploit multiple communities of practice working together. It is in this context that we shall examine semantic integration as an emerging enterprise IT community of practice that will revolutionize data management as we know it.

Semantics is an often misunderstood term, even moreso in regards to its technical applications. In philosophy, semantics refers to the study of meaning. The representation and dissemination of meaning, though, is what IT is all about. Every data element, every character in a string, every variable in an equation - they all express meaning in one form or another. Furthermore, that meaning is enhanced through frameworks of syntax and grammar as well as through countless explicit and implicit relationships. All system design is predicated upon a contract of shared understanding between stakeholders, developers and service providers; when something goes wrong, this is often the first place to look.

There are a number of specific standards and tools that have emerged over the past few years to support semantic integration; however, first we need to examine the problem space from a philosophical and business level. To understand how Semantics can be used to facilitate enterprise integration, we must first understand how Semantics relates to the practice of IT. Semantics is heavily focused upon hierarchies of meaning and relationships and, as one might expect, semantics has its own hierarchy.

Figure 1 no doubt contains some terms that are already familiar: vocabularies, taxonomies, ontologies and so forth. There is a new term depicted though, and this term is critical to the successful application of the other concepts to enterprise IT. The new term is called a “semantic set.” The semantic set is borne out of the recognition that no organization, regardless of how specialized it might be, is wholly dependent on one single vocabulary, taxonomy or ontology. Let’s also explore what these terms signify for us in their enterprise integration context.

• Vocabulary – This is the atomic-level view and is analogous to a data entity.
• Taxonomy – This includes the vocabulary, is a straightforward hierarchy and is analogous to earlier database management system (DBMS) design paradigms.
• Ontology – This includes both vocabulary and taxonomy and represents a structure that expresses both a hierarchy and a set of relationships between vocabulary elements within that hierarchy. This is roughly analogous to the design paradigms involved in relational database technology, although a schema is not necessarily an ontology and tends to be restricted to the system level.

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