# AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments
## 5 High Availability of Human-Computer Interaction  
With the changing needs of animal behavior experiments, commercial tools or independent experimental data management systems have gradually become unable to meet researchers' data retrieval and analysis needs. The main reason is that the interactivity of commercial tools or independent experimental data management systems could be better, and there are problems such as limited query syntax, pre-written queries, lack of context understanding, and strict format requirements. In order to improve the usability of human-computer interaction in data retrieval and analysis, the platform combines text-to-SQL-related algorithm models into natural language query interface services in the form of plug-ins and integrates them into the platform. Figure 4: Principle of ST-GCN algorithm, where GCN is spatial graph convolution and TCN is temporal graph convolution. Natural language query interface service can bring much convenience to the human-computer interaction of the platform. First, the natural language query interface service can provide researchers with more free query methods. Traditional database queries need to write SQL statements according to specific syntax and structure, which limits users' query methods. Researchers can use their familiar vocabulary and expressions for querying without being restricted to a specific query syntax. Secondly, the natural language query interface service can provide dynamic query functions. Researchers can adjust the query conditions according to real-time needs without writing fixed SQL statements in advance. For example, the user can say, \"Show the experimental mice that have been assisted to stand more than five times in the past three days\"; the natural language query interface service can understand the user's intent and generate and execute the corresponding SQL statement. The natural language query interface service makes human-computer interaction more flexible and natural through free querying methods, dynamic querying, and contextual understanding. Users can query in a way they are familiar with and make flexible adjustments according to real-time needs, thus improving the flexibility and adaptability of the interaction. In addition, the natural language query interface service brings significant advantages to human-computer interaction by improving query accuracy, lowering the use threshold, and providing a better user experience. Currently, the platform provides two algorithms as natural language query interface services for researchers: namely, RAT-SQL and RYANSQL [27; 28]. RAT-SQL encodes schema links and table structures based on Transformer by adding a relation-aware self-attention mechanism; Figure 5 illustrates the model structure of RAT-SQL. RAT-SQL transforms a database schema into a directed graph \(G_{q}\), describes known relationships by adding biases, and encodes associations between natural language questions and database schemas using name-based and value-based strategies. Eventually, these encoding results are fed into a tree Decoder and decoded according to the syntax rules of SQL to generate SQL statements. Due to the cross-linguistic issues, the platform also incorporates a cross-linguistic common sense knowledge graph and a cross-domain common sense knowledge graph (ConceptNet) [29] into the schema-concatenation phase of RAT-SQL, which results in improved accuracy of RAT-SQL execution for medical animal experiment information retrieval. Figure 5: The model structure of RAT-SQL. * N represents the number of Transformer layers. RYANSQL model mainly uses a sketch-based slot-filling method and marks the complex structure of SQL statements by SQL statement position code (SPC). RYANSQL divides the generation of SQL statements into two phases: sketch generation and slot filling. For nested statements, RYANSQL first splits the SQL statement into non-nested SELECT statement blocks and represents the relationship between the blocks by SPC.