Product Code Database
A semantic triple, or RDF triple or simply triple, is the atomic data entity in the Resource Description Framework (RDF) data model.http://www.w3.org/TR/PR-rdf-syntax/ "Resource Description Framework (RDF) Model and Syntax Specification" As its name indicates, a triple is a tuple that codifies a statement about semantic data in the form of subject–predicate–object expressions (e.g., "Bob is 35", or "Bob knows John").
<nowiki></nowiki>.
Given this precise representation, semantic data can be unambiguously Semantic query and reasoned about.
components of a triple, such as the statement "The sky has the color blue", consist of a subject ("the sky"), a predicate ("has the color"), and an object ("blue"). This is similar to the classical notation of an entity–attribute–value model within object-oriented design, where this example would be expressed as an entity (sky), an attribute (color) and a value (blue).
From this basic structure, triples can be composed into Semantic network, by using triples as objects or subjects of other triples—for example, Mike → said → (triples → can be → objects).
Given their particular, consistent structure, a collection of triples is often stored in purpose-built databases called .
In contrast to a traditional SQL database, an RDF triple store isn't created with a table editor. The preferred tool is a knowledge editor, for example Protégé. Protégé looks similar to an object-oriented modeling application used for software engineering, but it's focused on natural language information. The RDF triples are aggregated into a knowledge base, which allows external parsers to run requests. Possible applications include the creation of non-player characters within video games.
A more complex issue is a knowledge model's inability to predict future states. Even if all the domain knowledge is available as logical predicates, the model fails in answering What-if analysis. For example, suppose in the RDF format a room with a robot and table is described. The robot knows what the location of the table is, is aware of the distance to the table and knows also that a table is a type of furniture. Before the robot can plan its next action, it needs temporal reasoning capabilities. Thus, the knowledge model should answer hypothetical questions in advance before an action is taken.
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