## 2 Related Works
### Text Matching  
The fuzzy matching module proposed in this paper is constructed on the basis of a text matching algorithm. The purpose of text matching is to determine whether sentence pairs are semantically similar. Traditional text matching algorithms, such as TF-IDF (Term FrequencyInverse Document Frequency) [29], BM25 [30], Edit Distance [31], Simhash [32], etc., are mainly unsupervised. For instance, the TF-IDF algorithm calculates TF-IDF weight by evaluating the importance of a word to a document and judges the similarity between texts by calculating cosine similarity. This algorithm is efficient and interpretable, but the matching result is not ideal when dealing with complex texts because it only considers word frequency and does not highlight the deep semantic information. In recent years, with the development of deep learning technology, more and more researchers choose to use deep learning models to solve text matching tasks. Deep learning models excel at using context information to mine deep semantic information, which can effectively solve the shortcomings of traditional algorithms [33]. For example, [PERSON] et al. [34] proposed an unsupervised SIF (Smooth Inverse Frequency) method in 2016. This method represents sentence vectors by the weighted average of word vectors generated by Glove [35], Word2 Vec [8] and other methods and then modifies these sentence vectors by PCA (Principal Component Analysis) method. These sentence vectors can be used for textual classification, textual matching and other tasks. Moreover, the tBERT model proposed by [PERSON] et al. (2019) [36] predicts semantic similarity by combining semantic features extracted by the BERT model with topic features extracted by the Topic model. This model is prominent in domain-specific cases. In this experiment, consider the following three features:  
* The KBQA system needs to achieve efficient query. * For the data used in this work, the length of entities is relatively short, normally no more than 10 characters. * The query text input by the user in a KBQA system generally does not contain contextual information. Based on the above analysis, we choose to use the unsupervised Word2 Vec method to implement a fuzzy matching module. We generate the word embeddings of the words by using the Word2 Vec algorithm and obtain the sentence embeddings of entities through the accumulative average method. Then, we calculate the cosine similarity between vectors (sentence embeddings) for matching. In addition, according to the analysis of the NER model recognition results, we find that even those entities that are not correctly recognized by the NER model usually contain some useful information. Especially for the KBQA system, the query sentence input by the user is relatively short, and the topic entity of the sentence is generally unique. This feature makes the recognition results contain richer effective information. For example, a user enters the query \" \" \" \" \" \" \" \" (Who is [PERSON]'s wife?). The topic entity extracted by the NER model is \" \" \" \" \" \" ([PERSON]). However, the knowledge base only stores information about the topic entity \"  
bidirectional long-term dependencies in the sequence and has a strong nonlinear fitting ability which can be used to model for complex tasks. Compared with the traditional HMM (Hidden Markov Model), the MEMM (Maximum Entropy Markov Model) and other machine learning methods, the Bi-LSTM-CRF model has better performance and robustness [37; 38]. The SoftLexicon method makes full use of word and word sequence information while avoiding the negative effect of word segmentation errors. This method can be easily implemented by modifying the character representation layer of existing NER models such as the Bi-LSTM-CRF model. In this way, the performance of the Bi-LSTM-CRF model can be effectively improved on some tasks. At the same time, we also make improvements to this method for specific application scenarios. However, no NER model can achieve 100% recognition accuracy.