Stream Reasoning on Expressive Logics

TL;DR

This paper surveys reasoning techniques for expressive logics applied to streaming data, highlighting the challenges and proposing future research directions for efficient continuous reasoning on knowledge streams.

Contribution

It provides a comprehensive overview of existing reasoning methods for expressive logics in streaming contexts and identifies key areas for future research.

Findings

Limited work on reasoning procedures for streaming knowledge bases

Existing reasoning services are computationally expensive for large, continuous data streams

Highlights the need for optimized reasoning techniques in streaming applications

Abstract

Data streams occur widely in various real world applications. The research on streaming data mainly focuses on the data management, query evaluation and optimization on these data, however the work on reasoning procedures for streaming knowledge bases on both the assertional and terminological levels is very limited. Typically reasoning services on large knowledge bases are very expensive, and need to be applied continuously when the data is received as a stream. Hence new techniques for optimizing this continuous process is needed for developing efficient reasoners on streaming data. In this paper, we survey the related research on reasoning on expressive logics that can be applied to this setting, and point to further research directions in this area.