In order to succeed in business today, one has to read
and analyze a tremendous amount of information. This includes
reading news feeds, market reports, mailing lists or private
e-mail, searching for information on the Internet, communicating
with your colleagues, and so forth. The amount of information
is overwhelming, and it soon becomes difficult – if
not impossible -- to process it all. This phenomenon –
information overload -- has a huge impact on our efficiency.
Individuals in companies around the world experience information
overload. In an attempt to manage information, companies have
instituted systems of knowledge management. These systems
are designed to deal with a large volume of documents, including
reports, articles, patents, and so on. But do these systems
allow us to search, process, and manage information in a way
that is truly useful to us? Are these systems making things
better? Are we comfortable using them, and do they really
solve the problem of information overload?
The most frequent answer to all these questions is No.
But the problem of information overload is not in the amount
of information but in the tools we use to handle that information.
Knowledge management systems fail because they do not handle
information in different formats and structures in a way that
preserves the most important aspect of information - its meaning.
Using the semantic tools developed by EffectiveSoft, it is
possible to transform knowledge management systems into systems
that analyze and understand the meaning of information. A
semantic approach provides the solution to the problem of
information overload.
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| Semantic
Approach
To save you time and effort, EffectiveSoft has developed
intelligent tools that are highly effective in meaningful
information search and data structuring. To enhance our solutions,
we employ a semantic approach. This approach allows semantics-powered
software to perform an analysis of the meaning of words in
the text.
Our semantic approach has contributed to the development
of such unique features as:
Natural Language Query Statements
Summarization
Miscellaneous Usage
Cases
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Natural
Language Query Statements
Linguistic technologies make it possible for the computer to
"understand" human language. This allows a user to
communicate with search engines in the same way that people
actually communicate with each other. Old, simplistic search
engines required a user to transform his or her query into a
number of keywords. Today, you can just ask the search engine
a question, using natural language. Powered by modern linguistic
technologies, the semantics-based search engine will provide
you with results that have an extremely high degree of relevance
to your question.
An example of how Natural Language Query Statements are processed
can be found here: Proximator.
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Summarization
This solution, based on the semantic approach, allows
you to work with large documents. You can make a summary of
a text document and thus avoid having to read through its entire
contents. You can see the results of summarization in our online
Demo.
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Other Usage Cases
For implementation details on Information Retrieval, Natural
Language Text Synthesis, Question-answering System, and other
methods used in our products, see Solutions.
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Why Semantics?
Semantic Approach
