  Event recognition systems rely on properly engineered knowledge bases of
event definitions to infer occurrences of events in time. The manual
development of such knowledge is a tedious and error-prone task, thus
event-based applications may benefit from automated knowledge construction
techniques, such as Inductive Logic Programming (ILP), which combines machine
learning with the declarative and formal semantics of First-Order Logic.
However, learning temporal logical formalisms, which are typically utilized by
logic-based Event Recognition systems is a challenging task, which most ILP
systems cannot fully undertake. In addition, event-based data is usually
massive and collected at different times and under various circumstances.
Ideally, systems that learn from temporal data should be able to operate in an
incremental mode, that is, revise prior constructed knowledge in the face of
new evidence. Most ILP systems are batch learners, in the sense that in order
to account for new evidence they have no alternative but to forget past
knowledge and learn from scratch. Given the increased inherent complexity of
ILP and the volumes of real-life temporal data, this results to algorithms that
scale poorly. In this work we present an incremental method for learning and
revising event-based knowledge, in the form of Event Calculus programs. The
proposed algorithm relies on abductive-inductive learning and comprises a
scalable clause refinement methodology, based on a compressive summarization of
clause coverage in a stream of examples. We present an empirical evaluation of
our approach on real and synthetic data from activity recognition and city
transport applications.
