Semantic spectrum

The semantic spectrum describes a series of technologies for creating increasingly precise definitions for data.

At the low end of the spectrum is a simple binding of a single word or phase and its definition. At the high end is a full ontology (computer science) that specifies relationships between data elements using precise URLs for relationships and properties.

With increased specificity comes increase precision and the ability to use tools to automatically integrate systems but also increased cost to build and maintain the data model.

Some steps in the semantic spectrum include the following: # Glossary: A simple list of terms and their definitions. A glossary focuses on creating a complete list of the terminology of domain-specific terms and acronyms. It is useful for creating clear and unambigious definitions for terms and because it can be created with simple word processing tools, few tecnical tools are necessary. # controlled vocabulary: A simple list of terms, definitions and naming conventions. A controlled vocabulary frequently has some type of oversight process associated with adding or removing data element definitions to ensure consistancy. Terms are often defined in relationship to each other. # data dictionary: Terms, definitions, naming conventions and one or more representations of the data elements in a computer system. Data dictionaries often define data types, validation checks such as enuemtated values and the formal definitions of each of the enumerated values. # data model: Terms, definitions, naming conventions, representations and one or more representations of the data elements as well as the beginning of specification of the relationships between data elements including abstractions and containers. # taxonomy: A complete data model in an inheritance hierarchy where all data elements inherit their behaviors from a single super data element . The difference between a data model and a formal taxonomy is the arrangment of data elements into a formal tree structure where each element in the tree is a formally defined concept with associated properties. # (OWL). Ontologies frequently contain formal business rules formed in discrete logic statements that relate data elements to each another.

This concept is also sometimes referred to as the Ontology Spectrum or the Smart Data Contuniuum or Semantic Precision .

= Strategic Nature of Semantics =

Today, much of the world wide web is stored as HyperText Markup Language. Search engines are severly hampered by their inability to understand the meaning of published web pages. These limitations have created a movement called the Semantic web.

In the past, many organizations that created custom database application used isolated teams of developers that did not formally publish their data defintions. These teams frequently used internal data defintions that were incompatible with other computer systems. This made Enterprise Application Integration and Data warehousing extremely difficult and costly. Many organizations today require that teams consult a centralized data registry before new applications are created.

The job title of an individual that is responsible for coordinating an organization s data is a Data architect.

= Determining Location on the Semantic Spectrum =

Many organization today are building a Metadata registry to store their data definitions. The question of where they are on the semantic spectrum frequently arises. To Determin where your systems are some of the following questions are frequently useful.

Do you have a central location to go to to find and store data elements and their definitions

Do you have an approval process set up to approval data elements

Are coded Data Elements fully enumerated Does each enumeration have a full defintion

Is there a process in place to removed duplicate or redundant data elements from the metadata registry

= References =

The concept of the Semantic precision in business systems was popularized by Dave McComb in his book Semantics in Business Systems : The Savvy Managers Guide published in 2003 where he frequently uses the term Semantic Precision .

The first reference to this term was found at the [http://www.aaai.org/ AAAI] 1999 Ontologies Panel during July 18th and 19th. The [http://www.aaai.org/Workshops/1999/ws-99.html Ontology Workshop] was organizated by Adam Farquhar and Kilian Stoffel.

This discussion centered around a 10 level partition that included the following levels (listed in the order of increasing semantic precision): # Simple Catalog of Data Elements # Glossary of Terms and Definitions # Thesauri, Narrow Terms, Relationships # Informal Is-a relationships # Formal Is-a relationships # Formal instances # Frames (properties) # Value Restrictions # Disjointness, Inverse, Part-of # General Logical Constraints

Note that there was a special emphasis on the adding of formal is-a relationships to the spectrum which seems to have been dropped.

The company [http://cerebra.com Cerebra] has also popularized this concept by describing the data formats that exist within an enterprise in their ability to store semantically precise metadata. Their list includes: # HTML # PDF # Word Processing documents # Microsoft Excel # Relational Databases # XML # XML Schemas # Taxonomies # Ontologies

What the concepts share in common is the ability to store information with increasing precision to facilitate intelligent agents.