属性关系图匹配的神经网络实现及其应用
Attributed Relational Graph Matching Neural Network and its Application
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摘要: 属性关系图中存在着有向弧和多重弧,它是一种不对称的图,Hopfield网络是对称联接 的,通过定义节点属性距离和节点对关系弧属性距离,解决了不对称问题,从而把属性关系图 的匹配转化成可用Hopfield网络求解的形式.同时,又把误差校正思想引入了神经网络,使 网络可以实现随机语义网的匹配.Abstract: A new method of Error-calibrated and Attributed Relational Matchina Neural Network (EARGMNN) has been developed in this paper. Attributed Relational Graphs (ARG), there are direction arcs and multi-arcs. So ARG is asymmetric, but the Hopfield Net is symmetry. After redefining the distances of node feature and node-relational arc feature, we solved these asymmetry problem. At the same time, the idea of error-calibration has been introduced into neural network. Then the net can be used as random semantic net matching. The analogue annealing method has been introduced in EARGMNN model also, the test results are quite satisfactory.
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Key words:
- neural network /
- attributed relational graphs /
- analogue annealing
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