Logic Diffusion for Knowledge Graph Reasoning

Most recent works focus on answering first order logical queries to explore the knowledge graph reasoning via multi-hop logic predictions. However, existing reasoning models are limited by the circumscribed logical paradigms of training samples, which leads to a weak generalization of unseen logic. To address these issues, we propose a plug-in module called Logic Diffusion (LoD) to discover unseen queries from surroundings and achieves dynamical equilibrium between different kinds of patterns. The basic idea of LoD is relation diffusion and sampling sub-logic by random walking as well as a special training mechanism called gradient adaption. Besides, LoD is accompanied by a novel loss function to further achieve the robust logical diffusion when facing noisy data in training or testing sets. Extensive experiments on four public datasets demonstrate the superiority of mainstream knowledge graph reasoning models with LoD over state-of-the-art. Moreover, our ablation study proves the general effectiveness of LoD on the noise-rich knowledge graph.

UKnow: A Unified Knowledge Protocol for Common-Sense Reasoning and Vision-Language Pre-training

This work presents a unified knowledge protocol, called UKnow, which facilitates knowledge-based studies from the perspective of data. Particularly focusing on visual and linguistic modalities, we categorize data knowledge into five unit types, namely, in-image, in-text, cross-image, cross-text, and image-text. Following this protocol, we collect, from public international news, a large-scale multimodal knowledge graph dataset that consists of 1,388,568 nodes (with 571,791 vision-related ones) and 3,673,817 triplets. The dataset is also annotated with rich event tags, including 96 coarse labels and 9,185 fine labels, expanding its potential usage. To further verify that UKnow can serve as a standard protocol, we set up an efficient pipeline to help reorganize existing datasets under UKnow format. Finally, we benchmark the performance of some widely-used baselines on the tasks of common-sense reasoning and vision-language pre-training. Results on both our new dataset and the reformatted public datasets demonstrate the effectiveness of UKnow in knowledge organization and method evaluation. Code, dataset, conversion tool, and baseline models will be made public.