Semantic applications increase productivity in Public Sector

In the past decade, we've seen semantic applications emerge from the laboratory and into enterprise settings.  Forward-looking knowledge organizations are learning that semantic technologies can support a wide range of business processes. Semantic technologies go beyond a simple data retrieval by 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, involves the processing of recorded knowledge in a meaningful way – a way that represents how people think about things. These technologies use several different methods, including concept extraction, rule-based classification, dynamic classification, guided summarization and cross-language translation.   

Productivity improvements
Semantic analysis can improve the productivity of analytical decision making and information and knowledge processing, and increase the effectiveness of knowledge discovery. Organizations use semantic technologies to increase productivity by processing higher volumes of information than previously possible. Semantic technologies can reduce the time to process an institutional document from 30-40 minutes to less than two minutes.  At the same time, the quality of processing significantly improves, because we are explicitly codifying the mental processing steps. As a result, the opportunity costs of using subject matter expertise for standard information management tasks can be significantly reduced without risking access to organizational knowledge. 

Organizations increase productivity when they design and configure these technologies in sustainable enterprise applications, rather than as one-off projects. Semantic technologies are no longer the new IT toys. Nor are they "silver bullets" for tough knowledge challenges.  They are most effective when they're deployed to do what people do, but faster, more consistently, and/or with less effort.  This frees an organization's intellectual capital to do even more.

Where can an organization achieve the greatest productivity gains through semantic technologies?  For many, it's in information and knowledge processing of unstructured data or text.  For example, organizations have used:

• Rule-based concept extraction methods to capture key numerical indicators such as project numbers, contract numbers, unique IDs, digital object identifiers, ISBN (international standard book numbers) and other key financial numbers, with high levels of reliability and quality and minimal or no human effort.  
• Grammatical concept-extraction methods to characterize market reports, or new stories with high-precision sentiment analysis.  
• Grammatical concept extraction to construct descriptive maps of knowledge domains and dynamic clustering methods to illustrate the relationships of concepts within a domain. 
• Rule-based categorization methods to retrospectively organize large collections of critical business documents to support faceted search with minimal human investment, or to automatically and reliably classify current content to country focus. 

Achieving productivity improvements
Successful implementations result from deep modeling of business processes and integration of organizational knowledge sources, using well-planned project development life cycles and patient learning and fine-tuning of results.   

Deep modeling of business processes
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 seeking before you begin. Business architects have important roles to play in implementing semantic technologies. This is not just the role of engineers or programmers. 

Considered technology selections
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 isn't 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 a business process.  

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 have functional components – just like other applications. It is critical to ensure the semantic functions are fully supported by the technology you choose. Not all so-called semantic technologies are designed for enterprise use.  

Neither are all "semantic technologies" able to process unstructured information in meaningful ways.  Many technologies bundle NLP analysis so tightly with statistical processing that they are impossible to implement because 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 except as statistically occurring data bits. 

Organizational knowledge
A 21st century semantic technology can consume existing organizational knowledge.  However, a smart technology will not require this knowledge to be encoded as deeply embedded rules.  Organizational knowledge is always evolving. Smart technologies consume but don't control organizational knowledge sources.  Just as we no longer embed data into program code (we learned that lesson back in the 1990s), we should never embed organizational knowledge into semantic technology.  

Semantic technologies must 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, making it difficult to for either subject matter experts or knowledge professionals to work with and fine-tune them.  Subject matter experts should be the primary users of any design model for a semantic solution.   

Smart lessons, smart technologies
Knowledge organizations in the 21st century will find semantic analysis technologies to be a core, strategic application. They will be extensible, flexible systems configured for their purpose and designed to scale up or down.  Semantic analysis technology is not a "silver bullet" solution to all business challenges. In fact, most successful implementations of semantic technologies will be seamlessly and invisibly integrated into business processes.  

Successful experiences result from a deeper understanding of the semantic elements of your business processes, taking the time investigating the products, a willingness to invest in new and different knowledge roles, and a willingness to commit to ongoing support and expansion.  When organizations make firm commitments and investments, they can realize substantial productivity gains.

Bio: Denise Bedford is a Goodyear Professor of Knowledge Management at Kent State University, specializing in the economics of information and intellectual capital methods and management.  Formerly, she served as Senior Information Officer at the World Bank in Washington, D.C. and has worked at NASA, Intel Corp. and Stanford University.    

Goodyear Professor of Knowledge Management, Kent State University