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In the telecommunications field, the problem of data island is caused by the separation and isolation of data. They are distributed in different systems including the business support system (BSS), management support system (MSS), and operation support system (OSS). The common idea is to use global ID mapping to break data barriers. However, using the direct global ID mapping of raw data has the problems of large data scale and the inability to guarantee privacy and security. With this in mind, constructing and completing a metadata knowledge graph to fabric the data is a feasible approach. Considering the particularity of the telecom metadata knowledge graph and the need for hierarchical distinction in business and semantic abstraction, we propose a deep learning method and framework based on the spherical coordinate system. It can be extended to a poly-spherical coordinate system and add a pre-training process composed of word2vec and a clusterer. Experimental results show that our method achieves state-of-the-art on our dataset, MDCT and has excellent performance on two public datasets, FB15k-237 and WN18RR.

A Knowledge Graph Completion Method for Telecom Metadata Based on the Spherical Coordinate System