Abstract:

The power business involves a wide range of complex domain knowledge, usually existing in an unstructured form, including power system operation, energy management, power grid planning, etc. , which reduces the efficiency of intelligent services for power business. In order to improve service quality, a semantic retrieval algorithm for power business AI(Artificial Intelligence) Q&A based on the subject language model is proposed. A knowledge base for power business is established using a knowledge graph, providing rich semantic information and knowledge storage. Using TF-IDF(Term Frequency Inverse Document Frequency) to match the semantics of power business AI Q&A, a RWT BERT(Retrieval Augmented WaveNet Transformers) model is established based on semantic matching. This model is used to achieve more accurate semantic retrieval function for power business AI Q&A. The experimental results show that the proposed method has a recall and accuracy rate of over 96% , with an MRR(Mean Reciprocal Rank) value of up to 94% , indicating high retrieval accuracy and efficiency.

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