A semantic triple, or RDF triple or simply triple, is the atomic data entity in the Resource Description Framework (RDF) data model.[1] As its name indicates, a triple is a sequence of three entities that codifies a statement about semantic data in the form of subject–predicate–object expressions (e.g., "Bob is 35", or "Bob knows John").
This format enables knowledge to be represented in a machine-readable way. Particularly, every part of an RDF triple is individually addressable via unique URIs—for example, the statement "Bob knows John" might be represented in RDF as:
<nowiki>http://example.name#BobSmith12 http://xmlns.com/foaf/0.1/knows http://example.name#JohnDoe34</nowiki>.
Given this precise representation, semantic data can be unambiguously queried and reasoned about.
The 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 more complex models, 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 triplestores.
A relational database is the classical form for information storage, working with different tables, which consist of rows. The query language SQL is able to retrieve information from such a database. In contrast, RDF triple storage works with logical predicates. No tables nor rows are needed, but the information is stored in a text file. A RDF-triple storage can be converted into an SQL database and the other way around.[2] If the knowledge is highly unstructured and dedicated tables aren't flexible enough, semantic triples are used over classic relational storage.
In contrast to a traditional SQL database, an RDF triple storage isn't created with a table editor. The preferred tool is a knowledge editor, for example Protégé.[3] 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.[4]
One concern about triple storage is its lack of database scalability.[5] This problem is especially pertinent if millions of triples are stored and retrieved in a database. The seek time is larger than for classical SQL-based databases.
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 questions. 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.[6] Thus, the knowledge model should answer hypothetical questions in advance before an action is taken.