ID 原文 译文
20075 本体作为指导知识图谱数据构建的上层结构，在知识图谱技术中具有重要意义。 Ontology, as the superstructure of knowledge graph, has great significance in knowledge graph domain.
20076 本体在发展的过程中会形成结构上的冗余。 In general, structural redundancy may arise in ontology evolution.
20077 现有的本体消冗方法无法处理含有等价关系的本体结构，只能针对单一类属关系进行冗余的检测与消除。 Most of existing redundancy elimination algorithms focus on transitive redundancies while ignore equivalent relations.
20078 该文针对含有等价关系的本体提出一种基于超节点理论的消冗算法， Focusing on this problem, a redundancy elimination algorithm based on super-node theory is proposed.
20079 首先将相互等价的节点看作超节点，消除单一类属关系之间的的冗余； Firstly, the nodesequivalent to each other are considered as a super-node to transfer the ontology into a directed acyclic graph.Thus the redundancies relating to transitive relations can be eliminated by existing methods.
20080 然后还原等价节点，消除等价关系与类属关系之间的冗余。 Then equivalent relations are restored, and the redundancies between equivalent and transitive relations are eliminated.
20081 在计算机生成网络和真实网络上的实验和分析表明，该算法能够准确识别关系冗余，具有较高的稳定性和综合性能。 Experiments on both synthetic dynamic networks and real networks indicate that the proposed algorithm candetect redundant relations precisely, with better performance and stability compared with the benchmarks.
20082 该文提出一种载机偏航下基于广义相邻多波束(GMB)自适应处理的低空风切变风速估计的方法， This paper presents a method of low-altitude wind-shear speed estimation based on Generalized adjacent Multi-Beam (GMB) adaptive processing under aircraft yawing.
20083 该方法首先利用基于回波数据的杂波距离依赖性补偿方法对杂波进行距离依赖性矫正，估计出杂波协方差矩阵。 The clutter range-dependence compensation method based on echo data is first used to correct the range dependence of clutter for estimating the clutter covariance matrix.
20084 然后同时组合空域的相邻多个波束与时域的相邻多个多普勒通道来计算降维变换矩阵，并对待测距离单元内的雷达回波数据进行降维处理，进而构造GMB自适应处理器的最优自适应权矢量对降维后的回波数据实现自适应滤波。 Then the dimension-reduced transform matrix is calculated by combining adjacent multiple beams in the airspace and adjacent multiple Doppler channels in time domain simultaneously, and the radar echo data of the measured range bin is reduced in dimension, and then the optimal weight vector of the GMB adaptive processor is constructed to filter adaptively the dimension-reduced data.