CAKE : A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (2022)

Abstract: Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links between entities merely relying on fact-view data, ignoring the valuable commonsense knowledge. The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC’s performance. To address the above challenges, we propose a novel and scalable Commonsense-Aware Knowledge Embedding (CAKE) framework to automatically extract commonsense from factual triples with entity concepts. The generated commonsense augments effective self-supervision to facilitate both high-quality negative sampling (NS) and joint commonsense and fact-view link prediction. Experimental results on the KGC task demonstrate that assembling our framework could enhance the performance of the original KGE models, and the proposed commonsense-aware NS module is superior to other NS techniques. Besides, our proposed framework could be easily adaptive to various KGE models and explain the predicted results.

## introduction in recent years, knowledge graphs (kgs) such as freebase @xcite , dbpedia @xcite and nell @xcite have been widely used in many knowledge-intensive applications, including question answering @xcite , dialogue systems @xcite and recommender systems @xcite . however, the kgs constructed manually or automatically are inevitably incomplete, requiring kgc to infer new facts. s.a.) correct triple: david u.s.a. california rank: california 1 california 1 u.s.a. 2 3 commonsense canada ... unsatisfy satisfy satisfy ||edavid+rnationality-ecalifornia|| < ||edavid+rnationality-eu.s.a.|| ## methodology in this section, we introduce our novel and scalable cake framework. as shown in figure 2 , the entire pipeline consists of three developed modules: the automatic commonsense generation (acg) module, the commonsense-aware negative sampling (cans) module and the multi-view link prediction (mvlp) module. firstly, the acg module extracts the commonsense from the factual triples with the entity concepts via an instance abstraction mechanism ( § 3.2). then, the cans module employs the generated commonsense to produce the high-quality negative triples, which takes the characteristics of complex relations into account ( § 3.3). afterwards, our approach feeds the positive and the weighted negative triples into the kge model for learning entity and relation embeddings ( § 3.4). finally, the mvlp module conducts link prediction in a coarse-to-fine fashion by filtering the candidates in the view of commonsense and predicting the answer entities with kg embeddings from the candidates in the view of fact ( § 3.5). ## experiments and results ## conclusion in this paper, we propose a novel and scalable commonsense-aware knowledge embedding framework, which could automatically generate commonsense from kgs with entity concepts for the kgc task. we exploit the generated commonsense to produce effective and high-quality negative triples. on the other hand, we design a multi-view link prediction technique in a coarse-to-fine paradigm to filter the candidate entities in the view of commonsense and output the predicted results from the perspective of fact. the experiments on four datasets demonstrate the effectiveness and the scalability of our proposed framework and each module compared with the state-of-the-art baselines. furthermore, our framework could explain link prediction results and potentially assemble new kge models to improve their performance.