Three graph types ( static, temporal, multi-modal KGs) Static knowledge graph(KG) : is defined as SKG = {E, R, F }, where E, R, and F represent the sets of entities, relations, and facts. The fact is in a triplet format(e_h, r, e_t)∈ F, wheree_h, e_t ∈ E, andr ...
Talk3-Knowledge Graph and Its Applications in Meituan Waimai_哔哩哔哩_bilibiliwww.bilibili.com/video/BV1iD4y1U7R7/?spm_id_from=333.999.0.0&vd_source=fc21edf29ec66867f2af16c5351c2cf6 ——回答为什么要多模态信息在推荐系统里? 引入视觉、文本等信息形态;在电影推荐系统中,我们倾向于看先导片(视...
MIT license SNAG In this work, we introduce aUnified Multi-Modal Knowledge Graph (MMKG) Representation Frameworkthat incorporates tailored training objectives for Multi-modal Knowledge Graph Completion (MKGC) and Multi-modal Entity Alignment (MMEA). Our approach achieves SOTA performance across a compre...
[28]. While existing work about graph attention mechanisms all considersknowledge graphs, which is a type of heterogeneous graph from multi-modal data and aims to make the full & joint use of multi-modal data, they do not differentiate between the different kinds of links and nodes. This is...
MANS: Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training. However, existing NS methods are unsuitable when ...
Multi-Modal Knowledge Graph Construction and Application: A Survey论文阅读笔记 Alan Lee 2 人赞同了该文章 目录 多模态KG的定义、发展、意义、挑战, 从图像到符号构建KG/从符号到图像构建KG MMKG内应用/MMKG外应用 2定义和准备工作 两种MMKG A-MMKG N-MMKG 准备 多模态任务 图像字幕 视觉基础 可视化问答 ...
We construct a comprehensive multi-modal knowledge graph by enriching the user-item interaction graph with structured knowledge, images, and textual data. After encoding both user-item interactions and multi-modal information, we propose a self-supervised learning paradigm that eliminates the need for ...
论文题目:Beyond Entities: A Large-Scale Multi-Modal Knowledge Graph with Triplet Fact Grounding 作者:Jingping Liu, Mingchuan Zhang, Weichen Li , Chao Wang, Shuang Li, Haiyun Jiang, Sihang Jiang, Y…
Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenges due to the presence of different types of information, including neighboring entities, multi-modal attributes, and ...
has not yet received much attention compared to those applications. We introduce RECipe as a multi-purpose recipe recommendation framework with a multi-modal knowledge graph (MMKG) backbone. The motivation behind RECipe is to go beyond (deep) neural collaborative filtering (NCF) by recommending recip...