存在噪声关系的知识图充实与剪枝方法的基准研究

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Stefano Faralli, Andrea Lenzi, Paola Velardi
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引用次数: 0

摘要

在过去的几年里,知识图谱作为一种结构化的人类智能形式,引起了学术界和工业界的广泛关注。在这个非常活跃的研究领域中,一个被广泛探索的问题是链路预测,即基于节点属性和局部或全局图连接属性来预测两个节点是否应该连接。该领域的最新技术是基于图嵌入的技术。然而,KGs,特别是那些使用自动化或部分自动化技术获得的KGs,往往充满了噪音,例如,错误的关系,这使得链接删除问题与链接预测问题一样重要。在本文中,我们解决了三个主要的研究问题。第一个问题是在错误链接不断增加的情况下,不同知识图嵌入模型的真实有效性。第二步是评估那些能够有效预测未知关系的方法在识别错误关系时是否同样有效。第三是验证是否存在足够健壮的系统,可以在所有实验条件下保持首要地位。为了回答这些研究问题,我们进行了系统的基准研究,实验设置包括10个最先进的模型,3个具有不同结构属性的常见KG数据集和3个下游任务:广泛探索的链接预测和三重分类任务,以及不太受欢迎的链接删除任务。比较研究往往会产生相互矛盾的结果,同一系统的得分是高是低取决于实验背景。在我们的工作中,为了便于发现清晰的性能模式及其解释,我们选择和/或汇总性能数据以突出每个特定的比较维度:数据集复杂性、任务类型、模型类别和抗噪声鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Benchmark Study on Knowledge Graphs Enrichment and Pruning Methods in the Presence of Noisy Relationships
In the past few years, knowledge graphs (KGs), as a form of structured human intelligence, have attracted considerable research attention from academia and industry. In this very active field of study, a widely explored problem is that of link prediction, the task of predicting whether two nodes should be connected, based on node attributes and local or global graph connectivity properties. The state of the art in this area is represented by techniques based on graph embeddings. However, KGs, especially those acquired using automated or partly automated techniques, are often riddled with noise, e.g., wrong relationships, which makes the problem of link deletion as important as that of link prediction. In this paper, we address three main research questions. The first is about the true effectiveness of different knowledge graph embedding models under the presence of an increasing number of wrong links. The second is to asses if methods that can predict unknown relationships effectively, work equally well in recognizing incorrect relations. The third is to verify if there are systems robust enough to maintain primacy in all experimental conditions. To answer these research questions, we performed a systematic benchmark study in which the experimental setting includes ten state-of-the-art models, three common KG datasets with different structural properties and three downstream tasks: the widely explored tasks of link prediction and triple classification, and the less popular task of link deletion. Comparative studies often yield contradictory results, where the same systems score better or worse depending on the experimental context. In our work, in order to facilitate the discovery of clear performance patterns and their interpretation, we select and/or aggregate performance data to highlight each specific comparison dimension: dataset complexity, type of task, category of models, and robustness against noise.
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来源期刊
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research 工程技术-计算机:人工智能
CiteScore
9.60
自引率
4.00%
发文量
98
审稿时长
4 months
期刊介绍: JAIR(ISSN 1076 - 9757) covers all areas of artificial intelligence (AI), publishing refereed research articles, survey articles, and technical notes. Established in 1993 as one of the first electronic scientific journals, JAIR is indexed by INSPEC, Science Citation Index, and MathSciNet. JAIR reviews papers within approximately three months of submission and publishes accepted articles on the internet immediately upon receiving the final versions. JAIR articles are published for free distribution on the internet by the AI Access Foundation, and for purchase in bound volumes by AAAI Press.
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