When Successful Knowledge Graph Edits Displace Correct Answers: Rank-Level Locality beyond Parameter Support

2026-09-10

AI Summary

研究解决知识图谱嵌入编辑导致正确答案位移的问题，通过引入不同范围的排名位移审计及条件推导方法来减少位移效应。

Abstract

Editing a knowledge graph embedding (KGE) model to promote a desired answer can displace correct answers from the returned list. Locality tests based only on facts that reuse the edited parameter can miss this ranking effect. We introduce a common rank-displacement audit at three scopes: facts supported by the edited parameter, other correct answers to the target query, and correct answers across queries with the same relation. We also derive dimensional and geometric conditions for an update to improve the target while exactly preserving selected scores. On FB15k-237 with DistMult and ComplEx, direct promotion always moves the target into the top ten, but does so without damage in only 23.0--23.2\% of edits. Strict preservation causes no measured damage, yet succeeds in only 1.3--1.4\%. Support-regularized entity editing gives the highest joint success, 36.3--37.7\%, while rank-truncated preservation reaches 32.8--34.7\% and reduces the mean number of displaced answers from about 14 to 1.2. Experiments across dimensions, scorers, ranking conventions, and a learned editor show that locality depends on both the protected scope and the editing mechanism. KGE editing should therefore report correction success together with the incidence and severity of rank displacement.

Problem

• knowledge graph embedding
• rank displacement
• locality tests

Innovation

• rank-displacement audit
• support-regularized entity editing
• rank-truncated preservation