Semantics plays an important role on spatial scenes building and similarity contrast. Based on the description logic knowledge base (ontology) and multi-layer neural network, this paper simulates the procedure of human perception, measures the semantic similarity between spatial entities. In the Knowledge Base, spatial concepts are built by some description of space, time, and properties, most of these properties are representative, such as structure, shape and function and so on. This paper will describe the spatial entities semantics by function, part and attribute. Semantics description of similarity is calculated by each category. Then, introducing the artificial neural network algorithm during calculating the similarity, establishing the learning rules, optimizing the problem of weight value in similarity calculation process. This paper regard the waters as research object, train the artificial neural network by the calculated result and human subject, to mine knowledge, and verify the results. The result shows that this model can simulate cognition of human better, and calculate similarity of semantics easily and accurately.
['Yongyang Xu', 'Zhong Xie', 'Zhanlong Chen']
Research on semantics of entity space similarity measure based on artificial neural networks