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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").

Subject, predicate and object

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:

http://example.name#BobSmith12 http://xmlns.com/foaf/0.1/knows http://example.name#JohnDoe34.

Given this precise representation, semantic data can be unambiguously queried and reasoned about.

The basic semantic triple model

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.

Difference to relational databases

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]

Limitations

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.

References

^ http://www.w3.org/TR/PR-rdf-syntax/ "Resource Description Framework (RDF) Model and Syntax Specification"
^ Cuddihy, Paul and McHugh, Justin and Williams, Jenny Weisenberg and Mulwad, Varish and Aggour, Kareem S (2017). "SemTK: An Ontology-first, Open Source Semantic Toolkit for Managing and Querying Knowledge Graphs". arXiv:1710.11531 [cs.AI].{{cite arXiv}}: CS1 maint: multiple names: authors list (link)
^ Katis, Evangelos (2018). Semantic modeling of educational curriculum and syllabus (PhD). Technological Educational Institute of Crete.
^ Kluwer, Tina and Adolphs, Peter and Xu, Feiyu and Uszkoreit, Hans and Cheng, Xiwen (2010). Talking NPCs in a virtual game world. Proceedings of the ACL 2010 System Demonstrations. pp. 36–41.{{cite conference}}: CS1 maint: multiple names: authors list (link)
^ Jaroslav Pokorny (2015). "Graph Databases: Their Power and Limitations" (PDF). Computer Information Systems and Industrial Management. Computer Information Systems and Industrial Management. Lecture Notes in Computer Science. Vol. 9339. Springer International Publishing. pp. 58–69. doi:10.1007/978-3-319-24369-6_5. ISBN 978-3-319-24368-9.
^ Claudio Gutierrez and Carlos Hurtado and Alejandro Vaisman (2007). "Introducing Time into RDF". IEEE Transactions on Knowledge and Data Engineering. 19 (2). Institute of Electrical and Electronics Engineers (IEEE): 207–218. doi:10.1109/tkde.2007.34. S2CID 9749119.