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The semantic spectrum describes a series of technologies for creating increasingly precise definitions for Data elements. In metadata, the term data element is an atomic unit of data that has: an identification such as a Data element name a clear Data element definition one or more Representation terms optional enumerated values In telecommunication, the term data element has the following components: A named unit of 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 that specifies relationships between data elements using precise URLs for relationships and properties. To meet Wikipedias quality standards, this article or section may require cleanup. ...
With increased specificity comes increased 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 unambiguous definitions for terms and because it can be created with simple word processing tools, few technical 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 consistency. 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 enumerated 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 arrangement of data elements into a formal tree structure where each element in the tree is a formally defined concept with associated properties.
- ontology: A complete, machine-readable specification of a conceptualization using URLs for all data elements, properties and relationship types. The W3C standard language for representing ontologies is the Web Ontology Language (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 Continuum or Semantic Precision. A glossary is a list of terms with the definitions. ...
A controlled vocabulary (also called a taxonomy) is a carefully selected list of words and phrases, which are used to tag units of information so that they may be more easily retrieved by a search. ...
A data dictionary is a set of metadata that contains definitions and representations of data elements. ...
A data model is a model that describes in an abstract way how data is represented in a business organization, an information system or a database management system. ...
Taxonomy (from Greek verb tassein = to classify and nomos = law, science, cf economy) may refer to: the science of classifying living things (see alpha taxonomy) a classification Initially taxonomy was only the science of classifying living organisms, but later the word was applied in a wider sense, and may also...
To meet Wikipedias quality standards, this article or section may require cleanup. ...
The World Wide Web Consortium (W3C) is a consortium that produces standards—recommendations, as they call them—for the World Wide Web. ...
OWL is an acronym for Web Ontology Language, a markup language for publishing and sharing data using ontologies on the Internet. ...
Strategic Nature of Semantics
Today, much of the world wide web is stored as Hypertext Markup Language. Search engines are severely hampered by their inability to understand the meaning of published web pages. These limitations have created a movement called the Semantic web. In computing, HyperText Markup Language (HTML) is a markup language designed for the creation of web pages and other information viewable in a browser. ...
The Semantic Web is a project that intends to create a universal medium for information exchange by giving meaning (semantics), in a manner understandable by machines, to the content of documents on the Web. ...
In the past, many organizations that created custom database application used isolated teams of developers that did not formally publish their data definitions. These teams frequently used internal data definitions 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. To meet Wikipedias quality standards, this article or section may require cleanup. ...
A data warehouse is a record of an enterprises past transactional and operational activities, stored in a database. ...
The job title of an individual that is responsible for coordinating an organization's data is a Data architect. A Data Architect is a job title associated with a person within an organization responsible for making sure the organizations strategic goals are optimized through the use of enterprise data standards. ...
Determining Location on the Semantic Spectrum Many organizations today are building a Metadata registry to store their data definitions and to perform Metadata publishing. The question of where they are on the semantic spectrum frequently arises. To determine where your systems are, some of the following questions are frequently useful. A Metadata Registry is a central place where metadata definitions are stored and maintained. ...
Metadata publishing is the process of making metadata Data elements available to external users, both people and machines. ...
- Is there a centralized glossary of terms for the subject matter?
- Does the glossary of terms include precise definitions for each terms?
- Is there a central repository to store data elements that includes data types information?
- Is there an approval process associated with the creation and changes to data elements?
- Are coded data elements fully enumerated? Does each enumeration have a full definition?
- Is there a process in place to removed duplicate or redundant data elements from the metadata registry?
- Is there one or more classification schemes used to classify data elements?
- Are document exchanges and web services created using the data elements?
- Can the central metadata registry be used as part of a Model-driven architecture?
- Are their staff members trained to extract data elements are reuse metadata structures?
In metadata, the term data element is an atomic unit of data that has: an identification such as a Data element name a clear Data element definition one or more Representation terms optional enumerated values In telecommunication, the term data element has the following components: A named unit of data...
A web service is a collection of protocols and standards used for exchanging data between applications. ...
The Model-driven architecture (MDA) is a software design methodology, proposed and sponsored by the Object Management Group. ...
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 AAAI 1999 Ontologies Panel during July 18th and 19th. The Ontology Workshop was organized 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 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.
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