Triples

In data science, triples refer to a fundamental data structure used in the semantic web and knowledge representation. A triple consists of three parts: a subject, a predicate, and an object. The subject is typically a resource or entity, the predicate represents a relationship or attribute of the subject, and the object is the value or object of the predicate. Triples are used to represent knowledge in a machine-readable format, allowing for efficient processing and querying of large amounts of data. They are a key component of the Resource Description Framework (RDF), which is a standard for representing and exchanging data on the web.

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Asynchronous: Asynchronous refers to a method of processing data where tasks are executed independently of each other, without waiting for the completion of the previous task. In other words, asynchronous processing allows multiple tasks to be executed s ... Read More >>
Descriptive Measures: Descriptive measures are statistical values that summarize and describe the main features of a dataset. These measures provide insights into the central tendency, variability, and shape of the data distribution. Common descriptive measures ... Read More >>
Logical Rules: Logical rules refer to a set of principles or guidelines that are used to make decisions or draw conclusions based on a given set of data. In the context of data science and artificial intelligence, logical rules are often used in rule-base ... Read More >>

Triples

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