Customer Spending Fact as OrdLabTree

(Atom 'Peter Miller' (op 'spent at least') '5000 euros' 'in the previous year')
"Peter Miller spent at least 5000 euros in the previous year."

Customer Spending Fact as OrdLabTree - Notes

Datalog RuleML, as a markup language, can conveniently serialize relational information where all of the columns are natural-language phrases. To explain the Datalog features, we will develop a small example formalizing natural-language business rules in RuleML. This example correspond to the 'Eligibility' Category of Terry Moriarty's Business Rule Classification.

The structure of this natural language sentence can be visualized with a kind of linguistic parse tree, which we call an Order-Labeled (OrdLab) Tree. Here tags label both inner nodes (oval, RDF-like anonymous resources), e.g. with 'Atom' (atomic formula), and leaf nodes (rectangular, RDF-like literals containing PCDATA), e.g. with 'Rel' (relation constant), 'Ind' (individual constant), or 'Data' (data constant). The edge connecting the 'Atom' node to the 'Rel' node is labelled 'op' to distinguish it from the edges corresponding to arguments. An infix ordering for the operator is used to mimic the natural language ordering in the original sentence.

OrdLab trees were introduced in "A Web Data Model Unifying XML and RDF"