Neural symbolic reasoning with knowledge graphs:Knowledge extraction,relational reasoning,and inconsistency checking

Knowledge graphs(KGs)express relationships between entity pairs,and many real-life problems can be formulated as knowledge graph reasoning(KGR).Conventional approaches to KGR have achieved promising performance but still have some drawbacks.On the one hand,most KGR methods focus only on one phase of the KG lifecycle,such as KG completion or refinement,while ignoring reasoning over other stages,such as KG extraction.On the other hand,traditional KGR methods,broadly categorized as symbolic and neural,are unable to balance both scalability and interpretability.To resolve these two problems,we take a more comprehensive perspective of KGR with regard to the whole KG lifecycle,including KG extraction,completion,and refinement,which correspond to three subtasks:knowledge extraction,relational reasoning,and inconsistency checking.In addition,we propose the implementation of KGR using a novel neural symbolic framework,with regard to both scalability and interpretability.Experimental results demonstrate that our proposed methods outperform traditional neural symbolic models.

ARM3D:Attention-based relation module for indoor 3D object detection

Relation contexts have been proved to be useful for many challenging vision tasks.In the field of3D object detection,previous methods have been taking the advantage of context encoding,graph embedding,or explicit relation reasoning to extract relation contexts.However,there exist inevitably redundant relation contexts due to noisy or low-quality proposals.In fact,invalid relation contexts usually indicate underlying scene misunderstanding and ambiguity,which may,on the contrary,reduce the performance in complex scenes.Inspired by recent attention mechanism like Transformer,we propose a novel 3D attention-based relation module(ARM3D).It encompasses objectaware relation reasoning to extract pair-wise relation contexts among qualified proposals and an attention module to distribute attention weights towards different relation contexts.In this way,ARM3D can take full advantage of the useful relation contexts and filter those less relevant or even confusing contexts,which mitigates the ambiguity in detection.We have evaluated the effectiveness of ARM3D by plugging it into several state-of-the-art 3D object detectors and showing more accurate and robust detection results.Extensive experiments show the capability and generalization of ARM3D on 3D object detection.Our source code is available at https://github.com/lanlan96/ARM3D.