Knowledge Graph Embedding by Adaptive Limit Scoring Loss Using Dynamic Weighting Strategy (2022)

Abstract: Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for predicting missing links in knowledge graphs. Designing a strong and effective loss framework is essential for knowledge graph embedding models to distinguish between correct and incorrect triplets. The classic margin-based ranking loss limits the scores of positive and negative triplets to have a suitable margin. The recently proposed Limit-based Scoring Loss independently limits the range of positive and negative triplet scores. However, these loss frameworks use equal or fixed penalty terms to reduce the scores of positive and negative sample pairs, which is inflexible in optimization. Our intuition is that if a triplet score deviates far from the optimum, it should be emphasized. To this end, we propose Adaptive Limit Scoring Loss, which simply re-weights each triplet to highlight the less-optimized triplet scores. We apply this loss framework to several knowledge graph embedding models such as TransE, TransH and ComplEx. The experimental results on link prediction and triplet classification show that our proposed method has achieved performance on par with the state of the art.

## introduction knowledge graphs are usually collections of factual triplets -(head entity, relation, tail entity), also known as (subject, predicate, object), which represent human knowledge of the real world in a structured way. there are some outstanding knowledge graphs, such as wordnet @xcite , freebase @xcite , dbpedia @xcite , @xcite . they have gained widespread attention for their successful usage in various applications, e.g., question answering @xcite 2019 recommendation systems @xcite , medical science @xcite , etc. similar to word embedding, knowledge graph embedding is one of the basic research fields of knowledge graph, which can be applied to tasks such as knowledge graph completion @xcite , triplet classification @xcite , search personalization @xcite . for a knowledge graph embedding model, there are two major components, the scoring triplets and the optimizing loss function. in the last few years, negative sampling with margin-based ranking loss framework has been commonly used for modelling knowledge graph embedding. in this framework, a positive triplet (ℎ, 𝑟, 𝑡) can get its score 𝑆 𝑝 = 𝑓 𝑟 (ℎ, 𝑡), and the corresponding negative triplet (ℎ ′ , 𝑟, 𝑡 ′ ) score value is 𝑆 𝑛 = 𝑓 𝑟 (ℎ ′ , 𝑡 ′ ), where 𝑓 𝑟 is the scoring function. finally, optimize the margin-based ranking loss function 𝑚𝑎𝑥(0, 𝜇 + 𝑆 𝑝 -𝑆 𝑛 ). in 𝑚𝑎𝑥(0, 𝜇 + 𝑆 𝑝 -𝑆 𝑛 ), increasing 𝑆 𝑝 is equivalent to reducing 𝑆 𝑛 . we argue that this symmetric optimization manner is prone to the following two problems. ## the proposed methods in this section, we firstly present adaptive limit scoring loss 𝐿 𝐴𝑆 for optimizing knowledge graph embedding models. secondly, we introduce different metrics of our loss for optimization according to the positioning method of the circle center.