s book summarizes more than a decade of his research on knowledge representation for narrative text. The centerpiece of Zarri's work is the Narrative Knowledge Representation Language (NKRL), which he describes and compares to other competing theories. In addition, he discusses how to model the meaning of narrative text by giving many real-world examples. NKRL provides three different components or capabilities: (a) a representation system, (b) inferencing, and (c) an implementation. It is

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... mented via a Java-based system that shows how a representational theory can be applied to narrative texts. The book consists of five chapters and two appendices. Chapter 1 introduces the basic principles of NKRL. The chapter first defines the focus on nonfiction narratives by contrasting the domain with fictional narratives, for example, a novel. Zarri chooses n-ary predicates in order to represent events formally. He argues for a neo-Davidsonian knowledge representation following Schank (1980), Schubert (1976), and others, and at the same time he sets his approach apart from the knowledge representation proposals one can find in Semantic Web representation languages such as RDF and OWL. However, Zarri emphasizes that NKRL, despite its similarity to conceptual graphs (Sowa 1999), is more focused on practical applications. The chapter concludes by introducing so-called templates in an attempt to demonstrate the practical usefulness of NKRL. Chapter 2 provides an in-depth description of NKRL. Four connected components are introduced: r The definitional component provides a hierarchy of abstract concepts (e.g., artifact, company, activity) called HClass (hierarchy of classes). r The descriptive component is a hierarchy of event types called HTemp (hierarchy of templates) commonly found in the domain of non-fiction narratives (e.g., moving an object, producing a task or activity).