What is it? A group of technologies used to represent the meaning, associations, and know-how about the use of things separately from data and software.
When and why do I need it? Whenever there is ambiguity and inaccuracy in terminology and reporting.
Where do I apply it? Semantics are applicable to all information systems.
Who in my industry uses it? Semantics is an emerging technology dominated by startups and large agency thought leaders in order to reduce cost and enable new business capabilities.
How we do it!
Semantisys implements Semantic technology using proprietary methodologies and off-the-shelf: artificial intelligence; machine text processing; and vocabulary data management components.
Semantics is the study of meaning. We are drowning in data, but lack a complete understanding of the meaning of the data that we are immersed in. The complexities of human language, such as multiple meanings for one term, and multiple terms that express one concept are quickly negotiated by children, but presents a significant challenge to machine understanding. Understanding the meaning or semantics is often critical. The Department of Defense requests a tank, is this an oil tank or an artillery tank? A web search is entered, how the machine comprehends the semantics of the search terms will determine the value of the returned results. In machine language translation, understanding the question is a prerequisite to receive an informed reply. The most common semantic problem found within the enterprise is the lack of agreed upon meaning of common and mission critical terms. This problems is so severe and pervasive that several Federal Civilian and Department of Defense agencies now offer guidance in this area.
Federal agency guidance begins with an emphasis on controlled vocabularies. Implementation of these vocabularies is accomplished through a variety of means and must be tailored to the circumstances of the particular agency. One well established means for an agency to develop a controlled vocabulary is through a program of Meta data management. Meta-data, or data about data, is used to describe and define the meaning of each vocabulary data element. This meta data descriptive information will ideally include how data values relate to each other. Data relationships includes alternative meaning and relationships to other data values. Relationships to other data values are captured such as dataA IsUsedBy dataB; dataC I IsDerivedFrom dataD. The tool used to define and model such unique relationship's is called Ontology. Common vocabularies and Ontologies describing the relationships of these terms provides the fundamental meta-data architecture for automated machine understanding of incoming streams of data. With this architecture in place, new knowledge and new conclusions and be automatically inferred from data streams.
Semantic technology has caught popular attention. When applied to web data Semantic Technology is called Web 3.0. and focuses on identifying relationships between data located in web pages. These data relationships are defined using well defined standards. Once the data relationships are properly defined in a machine readable format, machine inference engines are then invoked to derive conclusions and logical deductions from the data and it's relationships. This capability to infer new information from existing data is a critical business value of Semantic Technology. Semantisys applies Semantic technologies throughout the entire information technology stack in order to derive the full potential of machine inference to solve business requirements. Semantisys specializes in the Semantic applications to SOA.
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