Relation representation based on private and shared features for adaptive few-shot link prediction

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Weiwen Zhang, Canqun Yang
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引用次数: 0

Abstract

Although Knowledge Graphs (KGs) provide great value in many applications, they are often incomplete with many missing facts. KG Completion (KGC) is a popular technique for knowledge supplement. However, there are two fundamental challenges for KGC. One challenge is that few entity pairs are often available for most relations, and the other is that there exists complex relations, including one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N). In this paper, we propose a new model to accomplish Few-shot KG Completion (FKGC) under complex relations, which is called Relation representation based on Private and Shared features for Adaptive few-shot link prediction (RPSA). In this model, we utilize the hierarchical attention mechanism for extracting the essential and crucial hidden information regarding the entity’s neighborhood so as to improve its representation. To enhance the representation of few-shot relations, we extract the private features (i.e., unique feature of each entity pair that represents the few-shot relation) and shared features (i.e., one or more commonalities among a few entity pairs that represent the few-shot relation). Specifically, a private feature extractor is used to extract the private semantic feature of the few-shot relation in the entity pair. After that, we design a shared feature extractor to extract the shared semantic features among a few reference entity pairs in the few-shot relation. Moreover, an adaptive aggregator aggregates several representations of the few-shot relation about the query. We conduct experiments on three datasets, including NELL-One, CoDEx-S-One and CoDEx-M-One datasets. According to the experimental results, the RPSA’s performance is better than that of the existing FKGC models. In addition, the RPSA model can also handle complex relations well, even in the few-shot scenario.

Abstract Image

基于私人和共享特征的关系表征,用于自适应少量链接预测
尽管知识图谱(KG)在许多应用中都具有重要价值,但它们往往并不完整,存在许多缺失的事实。知识图谱补全(KGC)是一种流行的知识补充技术。然而,KGC 面临两个基本挑战。一个挑战是大多数关系通常只有很少的实体对,另一个挑战是存在复杂的关系,包括一对多(1-N)、多对一(N-1)和多对多(N-N)关系。在本文中,我们提出了一种新的模型来完成复杂关系下的少量链接完成(FKGC),即基于私有和共享特征的自适应少量链接预测关系表示(RPSA)。在该模型中,我们利用分层关注机制来提取实体邻域的重要隐藏信息,从而改进实体的表示。为了增强少许关系的表示,我们提取了私有特征(即代表少许关系的每对实体的唯一特征)和共享特征(即代表少许关系的几对实体之间的一个或多个共性)。具体来说,私人特征提取器用于提取实体对中少数关系的私人语义特征。然后,我们设计了一个共享特征提取器,以提取少数几个参考实体对中的少数几个共享语义特征。此外,自适应聚合器还能聚合有关查询的少数几个关系表征。我们在三个数据集上进行了实验,包括 NELL-One、CoDEx-S-One 和 CoDEx-M-One 数据集。实验结果表明,RPSA 的性能优于现有的 FKGC 模型。此外,RPSA 模型还能很好地处理复杂的关系,即使是在少数几个镜头的情况下也是如此。
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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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