A relation detection method based on multi semantic similarity

Relation detection is a critical step of knowledge base question answering, which directly affects the quality of question answering. Among the existing methods, the encoding-comparison method extracts text global semantic information for matching, which often ignores the local semantic feature of text sequence. The interaction approach performs the comparison on low-level representations based on the sequence local information, which fails to consider the global semantic information of the input sequences. To solve the issues, this paper proposes a relation detection model based on Bert model and multi-semantic similarity considering global and local semantic information. First, our model introduces Bert as a text encoding layer to represent questions and relations as sequences of vectors. And then, a bi-directional long short-term memory (Bi-LSTM) layer with the attention mechanism is used to analyze the local semantic relevance and calculate the local similarity. Finally, our model uses a distance calculation formula to measure the global semantic relevance between questions and relations. The experimental results on two benchmark datasets, SimpleQuestions and WebQSP, show that the proposed model achieves the accuracy of 93.92% and 87.81% respectively, performs better than state-of-the-art approaches