RuleML

RuleML (Rule Markup Language) is a family of XML-based languages designed to express and exchange rules in knowledge-based and rule-driven systems. It provides a standard, machine-readable representation for rules, facts, queries, and derivations, enabling interoperability among heterogeneous inference engines, databases, and knowledge bases.

RuleML supports various rule types, including derivation and production rules, event-condition-action rules, and queries, with constructs for variables, built-ins, and data types. A typical RuleML rule expresses a body of conditions and a head that concludes a fact or action when the body holds, supporting both forward chaining and backward chaining reasoning depending on the dialect and engine used.

Historically, RuleML emerged from AI and Semantic Web communities in the late 1990s and early 2000s as an effort to standardize rule interchange across systems. Over time it evolved into a family of dialects and versions that interact with other rule-related standards. The RuleML initiative influenced later standards such as the W3C Rule Interchange Format (RIF), which sought similar goals within the Semantic Web stack.

RuleML has found use in academic research, rule-based integration projects, and some industry applications where interoperable rule expression is required. It is commonly discussed alongside RDF, OWL, and RIF in discussions of rule representation and exchange.