Business Process Semantics - an Opportunity for Convergence

Many different approaches to knowledge management exist, and passionate professionals differ on fundamental approaches, discovery and measurement disciplines and prescriptions for improvement. However, one unifying principle is the recognition that all businesses change and that improvement requires embracing change.

This article examines two approaches to improving a business and discusses how these approaches - business process management (BPM) and semantic Web technology - may be used together to improve business agility in the face of change.

Business Process Management

A business process is a set of operations or tasks that together accomplish a particular business goal, and BPM is the systematic study and optimization of business processes.

BPM has a long history, with roots in the rise of manufacturing in the early industrial age. The focus of most recent work is the definition and optimization of processes using computer software to handle some or all parts of a BPM lifecycle consisting of process design, modeling, execution, monitoring and optimization.

An example of a process in this modern sense is opening a bank account, which requires interactions among multiple participants, the exchange and storage of data in systems, the specific activities and decisions required by the bank’s business goals and policies as well as by external requirements, such as government regulations.

An organization can treat BPM primarily as a process modeling and improvement discipline, using software to model, measure and optimize processes. Alternatively, an organization can use specialized software, such as a business process management suite (BPMS), to manage processes as objects in a system. These suites provide process control and execution and tie the process to multiple applications and associated data.

The choice of tools and focus is driven by business goals for BPM. Understanding and measuring processes may give way to monitoring and improving processes. At its most fruitful, BPM is part of business transformation, allowing a business to substantially improve efficiency, productivity and customer satisfaction.

According to Gartner, a BPMS must provide 10 areas of functionality, including a process execution engine, a design environment, a registry/repository, business rules management and a set of tools to manage process lifecycle.¹

Most BPMS tools place both models and live process data in the registry/repository, and most treat the process model itself as the central design artifact. This means the data and metadata collected in a BPMS revolve around a process as an object to be controlled and optimized. Other important participants in a process include people, systems and documents, and these are modeled (and data is collected) in the BPMS as well.

The focus on the process of a BPMS often puts a prescriptive and sequential spin on data modeling. However, this is somewhat countered by the inclusion of business rules, their focus on policy as well as a descriptive or declarative approach to business or process decisions and the data needed to make them.

Businesses often use BPM as part of a services initiative to improve both business and IT agility, encapsulating processes, policies and services to improve reuse, gain flexibility and meet rapidly changing business requirements.

Semantic Web Technology

Semantics is the study of meaning in communication. Semantic data models, or ontologies, are formal models of domains of interest and have been explored for more than 25 years as part of artificial intelligence and knowledge management for computer reasoning, data classification and normalization and linguistic and text analysis.

The “semantic Web” was a term coined by Tim Berners-Lee in a paper of the same name in 2001.² Berners-Lee envisioned autonomous agents responding to situations based on well-defined requirements, both in the physical world and on the Web. Much of his vision is unrealized, but the computer-related ideas have evolved and merged with work on ontologies into a set of standards and software used both to describe Web pages and relationships as well as describe, exchange and reason against semantic data outside of the Web.

The core standards for the semantic Web, managed as part of the World Wide Web Consortium (W3C) semantic Web activity, include the resource description framework (RDF). The framework defines abstract statements and provides an XML-based implementation language and Web ontology language (OWL), which extends RDF to define ontologies.³

OWL and RDF implement an object-relational model allowing creation of a directed graph, a network of objects and relationships describing data. This model is potentially richer and more flexible than traditional data models used to implement relational databases.

RDF represents the English language statement, “Bob Jones works for Acme Corporation” as a single statement, where “Bob Jones” is the subject, “works for” is a predicate (also called attribute or relationship) and “Acme Corporation” is the object of the statement. This subject-predicate object pattern is called a triple, and it forms the basis of both RDF and OWL. In this context, both “Bob Jones” and “Acme Corporation” might be the names of abstract objects with numerous other statements representing attributes of the objects (age, location and whatever else is relevant to the particular systems).

OWL includes capabilities to restrict and extend object definitions, using classes and subclasses to define objects and their relations. Software might use OWL’s definitions to derive new information by inference against OWL and RDF data. For example, a shipping ontology that understands location as a concept might include hierarchies of locations like street/city/state/country, and shipping software using this ontology could derive additional location information about a package (“If the package is in Boston, then it is also in Massachusetts.”) while tracking that package to its destination.