Frequently Asked Questions

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Virtually anyone who has ever had the need or desire to take control of data, information and knowledge. Semantica products have application to every business, organization, industry or individual. Offering a superior interface and methodology for leveraging knowledge, Semantica can preserve human capital, protect intellectual property, facilitate education and learning, and foster valuable insight.

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Semantica products exploit advanced software technology and harness the principles of semantic networking theory. This begins by semantically defining and capturing each factoid or concept into a logically succinct relationship with another concept. Formatted as a bi-directional sentence, this concept-relationship-concept building-block of semantic networks is known as a triplet. From accumulation of potentially an infinite data base of triplets, Semantica offers a robust infrastructure for the interpretation, management and transfer of knowledge.

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Semantic Networking Theory, evolved from artificial intelligence research, is the best computer model to simulate how the brain learns and is cognitive of what it has learned. Like the mind, it not only defines a relationship between concepts, it builds a network of ways to get back to any factoid; that's the bi-directional function of triplets. Semantica expands instances of clearly defined relationships between concepts into a sophisticated technique for structuring and storing knowledge in an easily accessible manner, which we refer to as a Knowledge Structure.

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A Knowledge Structure is a highly interconnected foundation of knowledge that allows for the representation of contextual relationships between ideas and information. This approach lends itself well to visual representation, learning and interaction. In effect, it is both an implementation and extension of semantic networking theory.

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Very easy. In a way, knowledge structures author themselves. All that is required is to enter concepts and define their relationships to other concepts. In the process, new relationships suggest themselves intuitively to the author. Then the knowledge structure takes on a life of its own and begins to flow out of the author's singular creation into multiple dimensions, accessible and infinitely expandable by multiple authors.

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It can be, but most users start with data from either structured databases, web queries, or through processing unstructured text documents, then manually annotate those networks by hand, or automatically by fusing data from multiple other sources.

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Knowledge structures are interactive views of highly interconnected knowledge. A Concept Map is a way of taking slices from that Knowledge Structure, to concentrate on certain sections and study the flow of how the information is connected. Highly manipulative, Concept Maps can quickly display simple or complex scenarios, and search multiple paths between seemingly disparate concepts, not only in this Knowledge Structure, but in other knowledge structures and databases as well. From these displays, the user not only can see clear connections between ideas but envision how further connections can be made. Often even more important, the user can see that there are specific information gaps in the Knowledge Structure, so that they can focus their resources to fill those gaps, to complete the entire picture. Knowledge structures can be thought of as a superset of concepts maps.

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While relational and object-oriented databases are quite different, they both share a common weakness--they both require data to be well structured and understood before it can be stored or used. In addition, although both forms of database indicate relatedness, the specific relationship between items and objects are left to be inferred by the end-user. Queries against traditional databases require Boolean or SQL query syntax to return results, generally ranked by relevance. The user must then examine each individual result and reconstruct its relation to the query. Conversely, Semantica allows you to extract value immediately from unstructured and fuzzy data, by lowering the barrier to capturing this kind of data and providing an intuitive interface for knowledge browsing, analysis and transfer. Finally, if a new relationship is envisioned, the user can instantly create and enter that relationship into the entire semantic network, to expand their knowledge of any subject.