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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

August 7, 2013

Speaker: Dr. Adrian Paschke, Head of Corporate Semantic Web chair, Freie Universitaet, Berlin, Germany

Title: Reaction RuleML - Standardize Semantic Reaction Rules

Abstract: RuleML is a family of XML languages whose modular system of schemas permits high-precision (Web) rule interchange. The family's top-level distinction is deliberation rules vs. reaction rules. In this talk I will address the Reaction RuleML subfamily of RuleML. Reaction RuleML is a standardized rule markup/serialization language and semantic interchange format for reaction rules and rule-based event processing. Reaction rules include distributed Complex Event Processing (CEP), Knowledge Representation (KR) calculi, as well as Event-Condition-Action (ECA) rules, Production (CA) rules, and Trigger (EA) rules. Reaction RuleML 1.0 incorporates this reactive spectrum of rules into RuleML employing a system of step-wise extensions of the Deliberation RuleML 1.0 foundation.