  Reasoning is a fundamental capability for harnessing valuable insight,
knowledge and patterns from knowledge graphs. Existing work has primarily been
focusing on point-wise reasoning, including search, link predication, entity
prediction, subgraph matching and so on. This paper introduces comparative
reasoning over knowledge graphs, which aims to infer the commonality and
inconsistency with respect to multiple pieces of clues. We envision that the
comparative reasoning will complement and expand the existing point-wise
reasoning over knowledge graphs. In detail, we develop KompaRe, the first of
its kind prototype system that provides comparative reasoning capability over
large knowledge graphs. We present both the system architecture and its core
algorithms, including knowledge segment extraction, pairwise reasoning and
collective reasoning. Empirical evaluations demonstrate the efficacy of the
proposed KompaRe.
