Application of Neural Network Based Knowledge Graph in Vertical Industry
| Autor: | Zhao Xiaohong, Hao Zhang, Ren Tiancheng, Wang Wenting, Zhao Yang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 1584:012018 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1584/1/012018 |
| Popis: | Knowledge graph was mentioned first by Google, now is used to refer to a wide variety of large-scale knowledge bases, in both general areas and vertical industry areas. Techniques such as knowledge fusion, knowledge extraction, knowledge reasoning and knowledge expression are key techniques in knowledge graph which need to be researched urgently. Neural network technologies have achieved hugely these years, the technique has been readily applied to a variety of practical engineering problems such as pattern recognition, signal processing and control systems. In this work, we represent how to implement the neural network technique to knowledge reasoning technology, to achieve the completion of knowledge base, to predict hidden relationships among entities through reasoning inside a specific knowledge base. We also show the possibility of knowledge graph applied to one vertical industry, namely the electrified power grid industry. This paper shows that knowledge graph can apply to the whole power processing procedures, such as electric power producing, operating and marketing procession, and electric power equipment operation and maintenance, as well as customer service. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|