Text analytics = productivity gains
Today's semantic technologies now model human decision-making processes
With the proliferation of unstructured data, semantic applications have moved out of the laboratory and into enterprise contexts. 21st-century knowledge organizations are learning that semantic technologies can be designed, configured and implemented to support a wide range of business processes. Semantic technologies go beyond simple data retrieval. Today's semantic technologies, also known as text analytics, are smart, applying rigorous natural language processing (NLP) methods to model human decision-making processes, and – perhaps most importantly – they can integrate existing organizational knowledge.
Semantic technologies, in this context, involve the processing of recorded human knowledge in a meaningful way – in other words, in a way that represents how people think. These technologies use several different methods, including concept extraction, rule-based classification, dynamic classification, guided summarization and cross-language translation.
600% improvement in processing time
Advances in productivity are realized when organizations design and configure these technologies in sustainable enterprise applications rather than as one-time, one-off projects. Semantic technologies are no longer the new IT toy systems. Neither are they "silver bullets" for tough knowledge challenges. They are most effective when they're built to do what people do, only they do it faster and more consistently with less effort. This frees a knowledge organization's intellectual capital to do even more intelligent things.
Where can an organization achieve the greatest productivity gains through semantic technologies? Information and knowledge processing of unstructured data or text offer the greatest opportunities. For example, organizations have used:
How to realize productivity improvements
Sustainable use of semantic technologies means integrating them into everyday business processes. This means modeling and exploring existing business processes for appropriate "semantic opportunities." It means having an idea of the type of productivity gains you're aiming for before you begin. Business architects have important roles to play in implementing semantic technologies. This is not just the role of engineers or programmers.
Too often, technology decisions are based on a shallow understanding of how the technologies work and what they are designed to do. Managers often fall into the "I have a hammer, so everything looks like a nail" syndrome. It is not necessary to understand the statistical or parsing methods at a research level in order to decide whether a semantic technology has the functional components to support your business processes.
Sustainable semantic technologies are not out-of-the-box products. Rather, they are founded in well-designed architectures, including exposable and configurable knowledge bases, open matching rules, definable algorithms and interoperable semantic products. Semantic solutions, such as SAS Text Analytics, have functional components just as other applications do. Making sure that the semantic functions are fully supported by the technology you choose is critical.
Not all so-called semantic technologies are designed for enterprise use. Not all semantic technologies can process unstructured information in meaningful ways. Many technologies bundle the NLP analysis so tightly with statistical processing that they are impossible to implement given that they are "canned" applications. As a result, some are suited to only a small set of structured data processing problems. They may not be able to process unstructured data other than as statistically occurring data bits.
Evolving organizational knowledge
Semantic technologies need to be able to use different kinds of knowledge, represented as different kinds of structures. One size does not fit all types of knowledge. In fact, knowledge design and representation is a critical success (or failure) factor in implementing semantic technologies. Knowledge architects and knowledge engineers play critical roles in effective implementations.
Some semantic technologies require intense programming level support, making it difficult for subject matter experts or knowledge professionals to work with them and fine-tune over time. Subject matter experts should be the primary user of any design model for a semantic solution.
Smart lessons for smart technologies
Successful experiences result from a deep understanding of the semantic elements of your business processes, taking the time to investigate products, a willingness to invest in new and different knowledge roles, and a willingness to commit to ongoing support and expansion of the technologies. Productivity gains are substantial when organizations make a good commitment and investment.
This story appears in the Third Quarter 2011 issue of