Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts (2022)

Abstract: Knowledge Graph Embedding (KGE) has been proposed and successfully utilized to knowledge Graph Completion (KGC). But classic KGE paradigm often fail in unseen relation representations. Previous studies mainly utilize the textual descriptions of relations and its neighbor relations to represent unseen relations. In fact, the semantics of a relation can be expressed by three kinds of graphs: factual graph, ontology graph, textual description graph, and they can complement each other. A more common scenario in the real world is that seen and unseen relations appear at the same time. In this setting, the training set (only seen relations) and testing set (both seen and unseen relations) own different distributions. And the train-test inconsistency problem will make KGE methods easiy overfit on seen relations and under-performance on unseen relations. In this paper, we propose decoupling mixture-of-graph experts (DMoG) for unseen relations learning, which could represent the unseen relations in the factual graph by fusing ontology and textual graphs, and decouple fusing space and reasoning space to alleviate overfitting for seen relations. The experiments on two unseen only public datasets and a mixture dataset verify the effectiveness of the proposed method, which improves the state-of-the-art methods by 6.84% in Hits@10 on average.

## introduction knowledge graphs (kgs) such as freebase @xcite , dbpedia @xcite and yago @xcite contain large amounts of entities, relations and facts, which can be used to support many nlp tasks. knowledgedependent tasks rely heavily on the coverage of kgs. and the incompleteness of those kgs is an ## decoupling mixture-of-graphs experts this section describes in detail our proposed approach. the framework is shown in figure 2 . our method directly improves the effectiveness of previous kge models for unseen relations by making rich and accurate their representations. ## experiments we conduct extensive experiments with kgc task on several public datasets, and mainly evaluate the performance of the proposed framework on zeroshot relational learning. we also verify the proposed decoupling strategy to prevent overfitting on seen relations. to directly demonstrate the effectiveness of our method, we show a visualization of seen and unseen relations.