# A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language
## 6 Conclusions and Future Work  
Natural language semantic understanding enables computers to better comprehend user intentions and provides interfaces for user queries, thereby improving the user-friendliness, query expressiveness, and human-computer interaction of database systems, promoting intelligent geographic knowledge services. To address the issues of incomplete semantic information extraction and inaccurate intention recognition in natural language queries with spatiotemporal characteristics, this paper proposes the NL2 Cypher model for automated semantic encoding and understanding of spatiotemporal queries. Through inter-class coarse division and intra-class fine division strategies, the model effectively enhances the semantic understanding and intention recognition capabilities of spatiotemporal queries. The model achieves relatively high accuracy in query matching and logical form generation, and even some Cypher statements with partially mismatched logical forms can still provide correct results based on given data. Overall, the conversion from natural language spatiotemporal queries to structured Cypher queries proposed in this paper is effective and feasible. However, our method still has some limitations. For example, this study mainly relies on the dictionary information and glyph features of Chinese. However, recent studies have revealed that the phonetic and phonetic information of Chinese characters has potential value in improving task performance. Therefore, we will consider integrating the phonetic and phonetic information of Chinese characters into the character-level representation to achieve a comprehensive grasp of Chinese features. In future work, there are mainly the following areas that can be improved:  
(1) Enhance Model Generalization  
Although the NL2 Cypher model performs well on the current test dataset, user queries in practical applications may be more diverse and complex. Therefore, it is necessary to collect more diverse natural language spatiotemporal query data that should cover a variety of complex spatiotemporal relationships and the use of unknown vocabulary, especially datasets from foreign language environments such as Russian, Greek, or Arabic. By incorporating these diverse data, we can more effectively train and optimize the NL2 Cypher model to ensure that it can maintain high accuracy and stability in a wider range of query scenarios. (2) Improve Query Matching and Logical Form Accuracy  
While the model demonstrates good performance in query matching and logical form accuracy, there is still room for improvement. Future work should focus on more detailed model tuning and training as well as introducing additional constraints and rules to further enhance query matching accuracy and logical form consistency. Given the potential of the NL2 Cypher model in enhancing database query functions and improving user friendliness, we can subsequently cooperate with big data analysis companies, artificial intelligence, and machine learning companies to promote the development and application of technology. (3) Integrating multimodal data to improve query results  
Spatial data such as maps and satellite images are integrated into the NL2 Cypher model, and reinforcement learning techniques are introduced to more accurately predict user intentions and provide more personalized query suggestions based on user query preferences and patterns. At the same time, we are committed to developing and improving fairness assessment algorithms to ensure that query results are not only accurate and efficient but also meet the needs of different user groups. This research result not only has a far-reaching impact in China but also has broad promotion value in the international market. By promoting the NL2 Cypher model to the international market, it is expected to provide users in more countries and regions with an efficient and intelligent query experience. Conceptualization, methodology and validation are W.L., X.M. and D.M. Investigation and data curation are Y.C.