KompaRe: A Knowledge Graph Comparative Reasoning System
This addresses a gap in knowledge graph reasoning by complementing existing point-wise methods, potentially benefiting users in fields like data analysis or AI.
The paper tackles the problem of comparative reasoning over knowledge graphs, which infers commonality and inconsistency from multiple clues, and introduces KompaRe, a prototype system that demonstrates efficacy in empirical evaluations.
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.