搞清解码器选择的关键因素
本文提出配方控制的解码器审计（RCDA）用于结构知识图补全。以ComplEx和DistMult为主控对，辅以RotatE和TransE点检，在7个基准上评估。5个标准KG上ComplEx与DistMult的MRR差异在+0.005至+0.012之间。小KG上解码器效应更显著：Kinship中ComplEx优势达+0.143 MRR（6种子），UMLS中优势为+0.022 MRR（6种子）。YAGO3-10上，该配方下L=0的ComplEx在d=128时达到0.6971±0.0048 MRR。
Recipe-Controlled Decoder Audit for Structural Knowledge-Graph Completion
We present a recipe-controlled decoder audit (RCDA) for structural transductive knowledge-graph completion (KGC). The audit asks a simple reporting question: before attributing gains to an encoder or training recipe, what changes when the decoder is swapped under the same recipe? Using ComplEx and DistMult as the primary controlled pair, with targeted RotatE/TransE spot-checks, we evaluate seven benchmarks. On five standard KGs, ComplEx-vs-DistMult differences are modest but consistent under our recipe (+0.005 to +0.012 MRR), whereas CompGCN-style encoder effects vary more by dataset. On small KGs, decoder effects become the main diagnostic: Kinship shows a stable ComplEx advantage of +0.143 MRR (6 seeds), while UMLS favours ComplEx by +0.022 MRR in a clean 6-seed server rerun but reverses in an earlier provenance variant. We therefore treat small-KG decoder choice as recipe- and provenance-sensitive rather than as a fixed dataset winner. We further show that decoder choice interacts with encoder depth on WN18RR, and that under our recipe L=0 ComplEx on YAGO3-10 reaches 0.6971 +/- 0.0048 MRR at d=128. The result is a compact audit protocol: report matched decoder rows, log small-KG provenance, and sweep decoder x depth before making encoder-level claims.