Remembering the Important Things: Semantic Importance in Stream Reasoning

Reasoning and querying over data streams rely on the ability to deliver a sequence of stream snapshots to the processing algorithms.

These snapshots are typically provided using windows as views into streams and associated window management strategies.

In this work, we explore a general notion of \textit{semantic importance} that can be used for window management of RDF streaming data using semantically-aware processing algorithms.

Semantic importance exploits the information in RDF streams and surrounding ontologies for ranking window data in terms of its contribution to solution mappings.

We also consider how a stream window management strategy based on semantic importance could improve overall processing performance, especially as available window sizes decrease.

Associated Projects

Stream reasoning systems are typically built using a fixed-size data windows, into which new elements from the stream are continuously added, and from which other elements are dropped. Our project develops a new class of window management algorithms based on semantic importance, using RDF data semantics to rank the elements of the window for their importance. Using semantics achieves higher reasoning performance, particularly for smaller window sizes, and takes account of background knowledge in the domain.

AIM will advance streaming reasoning techniques to overcome a limitation in contemporary inference that performs analysis only over data in a fixed cache or a moving window. This research will lead to methods that continuously shed light on proposed hypotheses as new knowledge arrives from streams of propositions, with a particular emphasis on the effect that removing the expectation of completeness has on the soundness and performance of symbolic deduction platforms. The work will address challenges in sampling rates, cache maintenance, deductive reasoning, and ranking of conclusions.