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By Lauren Bielski
Despite signs of increased usage, the semantic Web, which is essentially about leveraging metadata to make Web pages machine readable, has yet to truly emerge.
Semantic Web specifications have not gained broad or deep adoption in the data warehouse or analytics areas that would seem to be a natural fit, says Forrester senior analyst James Kobielus. Despite the potential of combining business intelligence and the semantic Web, BI vendors aren't pushing the specs for their tools, apps, platforms or middleware. Nor is "the semantic vision" being heavily promoted by enterprise vendors.
A "killer app" might drive such adoption, but that app has not emerged.
"There is some enterprise activity -- that is, efforts to standardize and integrate data at various companies, but it is occurring in disconnected pockets," says Kobielus. "It isn't a broad movement."
Rather than an outright success or failure, the semantic Web is probably best views as a work in progress. But CIOs may want to pay attention to the seemingly esoteric technology, because efforts from consortia or potential competitors may spell out a way to get more from your data.
Kobielus and other experts contacted by CIOZone for this article agree that W3C-developed semantic Web specifications -- most notably, the Resource Descriptor Framework (RDF) and Web Ontology Language (OWL) -- have begun to gain some traction in commercial products.
Start-ups are offering ontology modeling tools, inference engines, RDF repositories, and other necessary components of semantic Web solutions. And while it's nowhere near mainstream, says Kobielus, "more users are starting to incorporate semantics-based approaches in their search, text analytics, enterprise content management, enterprise information integration (EII), and other mission-critical applications."
All semantic Web implementations use RDF as their core ontology language, though many also support OWL for its semantic richness. A growing number are implementing the RDF query language known as SPARQL; a mark-up format called GRDDL, short for Gleaning Resource Descriptions from Dialects of Languages; and related W3C specifications.
Ontologies are an explicit, formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest -- and the relationships among them. In other words, a formal representation of a set of concepts within a domain.
And these ontologies, or domain-related concepts, are beginning to support the following:
Semantic Modeling: In this approach, developers explicitly model semantics as RDF/OWL ontologies or related structures such as taxonomies, thesauri and topic maps. The ontologies are being used to drive creation of structured content that instantiates the entities, classes, relationships, attributes and properties defined in the ontologies. "This is the classic model of greenfield development of application data under the semantic Web paradigm," explains Kobielus.
Semantic Mediation: Here, developers explicitly model semantics as RDF/OWL ontologies, and use them to build mappings, transformations, and aggregations among existing, structured data sets. "This describes the typical use of semantic Web approaches within heterogeneous EII and other data integration projects," according to Kobielus.
Semantic Mining: In this case, developers use natural-language processing and pattern-recognition tools to infer and extract the implicit semantics from unstructured text sources. The extracted entities, relationships, facts, sentiments, and other artifacts are used to fashion RDF/OWL ontologies that drive the creation of indices, tags, annotations, and other metadata that layer a consistent semantic structure across the various items within an unstructured text store. This describes the typical use of the semantic Web in search and text mining/analytics environments.
Where Can Enterprise Apps Benefit?
It may not be widely used, but Kobielus has high hopes for the semantic Web. He sees several existing enterprise applications that could benefit from a semantic approach:
Enterprise content management (ECM): Semantic approaches can support more powerful discovery, indexing, search, classification, commentary, and navigation across heterogeneous stores of unstructured and semi-structured content. Semantic search -- driven by concepts, not mere text strings -- is regarded by many as the potential killer application of semantic Web technology, according to Kobielus. Indeed, many semantic Web vendors are primarily implementing the technology in search engines that leverage ontology-based concepts to improve search accuracy and reduce spurious hits.
Enterprise information integration (EII): Semantic approaches enable consolidated viewing, querying, and updating of structured data that has been retrieved from diverse sources. In fact, most commercial EII environments present an abstract semantic layer that mediates access to heterogeneous data, such as enterprise resource planning (ERP) and customer relationship management (CRM) applications, bringing it together in a common presentation-side schema. "A handful of enterprise integration vendors -- including Oracle/BEA and Red Hat/MetaMatrix -- have begun to support semantic Web standards, primarily through third-party software plug-ins," says Kobielus.
Enterprise service bus (ESB): Semantic approaches can facilitate multilayered application, process, and service interoperability across disparate environments. To date, there has been little production implementation of semantic Web standards in the enterprise service bus arena, points out Kobielus, though vendors such as Telcordia Technologies have adopted semantics, ontologies and RDF to describe the conceptual models implemented by application endpoints, agents, and intermediary nodes within ESB-like middleware approaches such as event stream processing.