信息系统(IS)是支持数据密集型应用程序的软件和硬件系统。《信息系统》杂志发表了有关信息系统的语言、数据模型、过程模型、算法、软件和硬件的设计和实现的文章。主题领域包括如ACM SIGMOD / PODS,VLDB,ICDE和ICDT / EDBT,以及来自数据挖掘/机器学习,与结构化数据协调的信息检索,互联网领域的数据相关问题以及云数据管理、业务流程管理、Web语义、视听信息系统、科学计算和数据科学。官网地址:http://dblp.uni-trier.de/db/journals/is/

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Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way. In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.

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Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficulties for KGC is a low resource problem. Previous approaches assume sufficient training triples to learn versatile vectors for entities and relations, or a satisfactory number of labeled sentences to train a competent relation extraction model. However, low resource relations are very common in KGs, and those newly added relations often do not have many known samples for training. In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available. We propose a general framework called Weighted Relation Adversarial Network, which utilizes an adversarial procedure to help adapt knowledge/features learned from high resource relations to different but related low resource relations. Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations. Experimental results show that the proposed approach outperforms previous methods regarding low resource settings for both link prediction and relation extraction.

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