The Semantic Web is a distributed model for publishing, utilizing and
extending structured information using Web protocols. One of the goals
of this technology is to automate the retrieval and integration of
data and to enable the inference of interesting results. This
automation requires logics and rule languages that make
inferences, choose courses of action, and answer questions. The
openness of the Web, however, leads to several issues including
inconsistencies, integration of diverse information, and determining
the trustworthiness of data. The goals of Web rule languages should,
therefore, include localizing inconsistencies, tracking information
sources, extracting relevant information from Web resources, and
capturing deduction traces. AIR is a Semantic Web-based rule language
that embodies these objectives by focusing on generating and
tracking explanations for its inferences and actions while conforming to
linked data principles. AIR explanations themselves are Semantic Web
data, allowing them to be easily re-used and extended for further reasoning. In this paper we present an
overview of AIR, discuss its potential as a Web rule language by
providing examples of how its features can be leveraged in different
application domains, and describe its semantics through translation to
stratified Logic Programming (LP) rules.

