计算机科学
相似性(几何)
匹配(统计)
弦(物理)
对象(语法)
订单(交换)
理论计算机科学
片段(逻辑)
人工智能
作者
Weixin Zeng,Xiang Zhao,Jiuyang Tang,Xuemin Lin
出处
期刊:International Conference on Data Engineering
日期:2020-04-20
被引量:21
标识
DOI:10.1109/icde48307.2020.00191
摘要
Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current solutions treat entities independently and fail to take into account the interdependence between entities. To fill this gap, we propose a collective EA framework. We first employ three representative features, i.e., structural, semantic and string signals, which are adapted to capture different aspects of the similarity between entities in heterogeneous KGs. In order to make collective EA decisions, we formulate EA as the classical stable matching problem, which is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks against state-of-the-art solutions, and the empirical results verify its effectiveness and superiority.
科研通智能强力驱动
Strongly Powered by AbleSci AI