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arXiv 2019年

作者： Zhang, Wen Paudel, Bibek Wang, Liang Chen, Jiaoyan Zhu, Hai Zhang, Wei Bernstein, Abraham Chen, Huajun College of Computer Science Zhejiang University Stanford University University of Zurich Department of Computer Science University of Oxford Alibaba Group China Alibaba Group AZFT Joint Lab for Knowledge Engine Department of Informatics University of Zurich Switzerland Zhejiang University AZFT Joint Lab for Knowledge Engine China

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments showthat iteratively learning embeddings and rules benefit each other during learning and prediction. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Benchmarking knowledge-driven Zero-shot Learning

arXiv

引用

arXiv 2021年

作者： Geng, Yuxia Chen, Jiaoyan Zhuang, Xiang Chen, Zhuo Pan, Jeff Z. Li, Juan Yuan, Zonggang Chen, Huajun College of Computer Science & HIC Zhejiang University 38 Zheda Rd Zhejiang Hangzhou310012 China Knowledge Engine Group AZFT Joint Lab 1818-2 Wenyi West Rd Zhejiang Hangzhou311000 China Department of Computer Science University of Oxford 15 Parks Rd OxfordOX1 3QD United Kingdom School of Informatics The University of Edinburgh 10 Crichton St EdinburghEH8 9AB United Kingdom NAIE Product Department Huawei Technologies Co. Ltd. 101 Software Avenue Nanjing210012 China

External knowledge (a.k.a. side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data. Several kinds of external knowledge, such as text and attribute, have been widely investigated, but they alone are limited with incomplete semantics. Some very recent studies thus propose to use knowledge Graph (KG) due to its high expressivity and compatibility for representing kinds of knowledge. However, the ZSL community is still in short of standard benchmarks for studying and comparing different external knowledge settings and different KG-based ZSL methods. In this paper, we proposed six resources covering three tasks, i.e., zero-shot image classification (ZS-IMGC), zero-shot relation extraction (ZS-RE), and zero-shot KG completion (ZS-KGC). Each resource has a normal ZSL benchmark and a KG containing semantics ranging from text to attribute, from relational knowledge to logical expressions. We have clearly presented these resources including their construction, statistics, data formats and usage cases w.r.t. different ZSL methods. More importantly, we have conducted a comprehensive benchmarking study, with two general and state-of-the-art methods, two setting-specific methods and one interpretable method. We discussed and compared different ZSL paradigms w.r.t. different external knowledge settings, and found that our resources have great potential for developing more advanced ZSL methods and more solutions for applying KGs for augmenting machine learning. All the resources are available at https://***/China-UK-ZSL/Resources_for_KZSL. © 2021, CC BY.

关键词： Image classification

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