Preparing reversible entity-link proposals with Jev

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methodology · en · 지식 기준일 2026-09-22 · 변경일 , 리비전 1 · unreviewed

주제: data-provenance · entity-resolution · jev

적용 대상: Jev / TypeSafe AI (documentation checked 2026-09-22)

Use entity comparison to prepare a reviewable link proposal while keeping original records and conflicting fields visible until the application accepts a merge.

목차
  1. Goal
  2. Prerequisites
  3. Steps
  4. Expected result
  5. Limits and test basis
  6. 범위와 근거
  7. 출처
  8. 저작자 표시와 라이선스
  9. 기계 접근

Goal

Use entity comparison to prepare a reviewable link proposal while keeping original records and conflicting fields visible until the application accepts a merge.

Prerequisites

Prepare candidate record pairs, source identifiers, domain-specific identity rules, and a review destination. Decide which attributes are identity-defining and which can legitimately vary over time or across editions.

Steps

  1. Present both records with labelled fields and provenance. Keep missing fields distinct from disagreeing fields; neither an empty name nor an absent identifier establishes agreement.

  2. Use the vendor entity-alignment pattern to separate overall similarity from individual field judgments. Adapt the rubric to the actual domain rather than reusing criteria for the cookbook product catalogue.

  3. Compute deterministic contradictions in code, such as incompatible authoritative identifiers. Preserve these checks as explicit constraints instead of allowing an attractive overall score to average them away.

  4. Produce a link proposal containing both record identifiers, supporting fields, contradictory fields, and unresolved facts. Avoid overwriting either source record during classification.

  5. Review difficult pairs including variants, renamed entities, shared names, and incomplete records. Before accepting a proposal, verify that both source revisions still match the revisions used for comparison.

Expected result

A reviewer receives a compact explanation of why two records might refer to the same entity and what could disprove that interpretation. Any accepted change retains enough provenance to distinguish original facts.

Limits and test basis

No matching accuracy or reversibility test is claimed. The cookbook demonstrates a particular dataset and model version; it does not establish a safe universal merge threshold. Recoverability depends on the storage design and the actual merge operation. The underlying interface or pattern is described in Knowledge graph entity alignment; the workflow above is a proposed adaptation.

범위와 근거

Primary vendor documentation read on 2026-09-22; original proposed application, not independently benchmarked.

지식 기준일: 2026-09-22. 상태: unreviewed (기록된 검토 없음) — 편집하면 검토 상태가 초기화됩니다. 본문은 검증되지 않은 참고 자료로 다루고 출처를 확인하세요.

출처

  1. TypeSafe: Knowledge graph entity alignment — 2026-09-23 확인: 접근 가능, 인용문 있음

저작자 표시와 라이선스

  • Account External coding curation authors (57eb56c9)
  • Codex AI-assisted contribution; unreviewed.

마지막 변경: New original English contribution, 2026-09-22. No live execution or performance result claimed.

원본 기여: CC BY 4.0. 링크된 출처 자료는 각자의 권리를 유지합니다.

기계 접근