RDF triplestore is a type of graph database that stores semantic facts. RDF, which stands for Resource Description Framework, is a model for data publishing and interchange on the Web standardized by W3C.
Being a graph database, triplestore stores data as a network of objects with materialised links between them. This makes RDF triplestore a preferred choice for managing highly interconnected data. Triplestores are more flexible and less costly than a relational database, for example.
The RDF database, often called a semantic graph database, is also capable of handling powerful semantic queries and of using inference for uncovering new information out of the existing relations.
In contrast to other types of graph databases, RDF triplestore engines support optional schema models, called ontologies. Ontologies allow for formal description of the data. They specify both object classes and relationship properties, and their hierarchical order.
The data in RDF triplestore is stored in the relationship
which is called a triple, hence the name triplestores. The triples are also referred to as ‘statements’ and ‘RDF statements’.
The subject->predicate->object format is able to take any subject or concept and connect it to any other object by using the predicate (verb) to show the type of relationship existing between the subject and the object.
For example, ‘Joe sells books’ can be stored as an RDF statement in a triplestore and describes the relationship between the subject of the sentence, Joe, and the object, books. The predicate “sells” shows how the subject and the object are connected.
The core concept of the RDF triplestore format as well as in the Linked Data paradigm is the Universal Resources Identifier (URI). URI is a single global identification system used in the Web, a kind of unique ID.
RDF triplestore databases are successfully used for managing Linked Open Data datasets, such as DBPedia and GeoNames, which are published as RDFs and are interconnected with one another. Linked Open Data allows for querying and answering federated queries much faster and for obtaining highly relevant search results.
The triplestore makes the efforts to query diverse and evolving data from different sources more cost-efficient and less time-consuming.
Since universal standards apply to RDF triplestore, they make moving data from one triplestore to another trivial.
RDF triplestore handle huge amounts of data, which improves the search and analytics powers of organizations. What’s more important is that triplestores are able to infer implicit facts out of the explicit statements. Inferencing relationships out of the original data, with the help of a semantic graph database, turns information into knowledge. This allows organizations to uncover hidden relationships across all their data.
Having gained more knowledge than competitors, enterprises can more easily scale up that knowledge into smarter solutions and have the upper hand in competition. The media & publishing, healthcare and life sciences, digital humanities and financial services sectors are already widely using RDF triplestore to manage unstructured and structured data.
Triplestores also help extract information and enrich content from unstructured data by text mining. After a text is extracted from any form of unstructured data, be it articles or documents, sentences are broken down into parts of speech. The important concepts and entities, such as proper nouns, are identified with dictionary word lists.
Semantic technology and machine learning algorithms classify and disambiguate between entities. By ‘learning’ the context and meaning of entities, the algorithms are able to disambiguate ‘Paris’, for example, whether it is referred to Paris, France, or Paris, Texas, or Paris Hilton, or Paris, the God in Greek mythology.
Apart from containing relationships, triples also demonstrate links between databases with structured data and documents that contain unstructured, free-flowing text. RDF triplestore, often referred to as graph database and graph db, links entities from databases to documents which mention those entities by denoting relationships from which they were extracted.
Graph databases, and RDF triplestore in particular, have various practical usages for organizations that aim to have context as well as content. Some of the uses are data integration, search and discovery, dynamic information products, personalized content and recommendations, and data visualization. These solutions, combined with knowledge discovery out of information from disparate sources, help organizations gain a competitive edge, create more value, and tap into new sources of revenues.