from tableqa.agent import AgentĪgent.query_db("how many deaths of age below 40 had stomach cancer?") Let's try to answer some questions from this dataset. You can see the file we are trying to query is a CSV (comma separated values) file with columns like Year, Nationality, Gender, Cancer Site, Death Count, and age. import pandas as pdĭf = pd.read_csv("cleaned_data/Cancer Death - Data.csv") !git clone Īnd now we'll take our first look at the data. ![]() ![]() Next, we'll access the sample data from the repository. Let's see a demo on how to use this tool for querying cancer death data obtained from the Abu Dhabi open platform.įirst, install the package and requirements. TableQA converts natural language queries to SQL queries in such a manner. A SQL query is built from several component statements which include conditions, aggregate operations, etc. TableQA: an AI-assisted tool for question answering on tabular dataĪs we saw earlier, one method for breaking down natural language into smaller components is converting them into SQL queries. We will take a look at such a tool below. Other solutions are not pre-trained on datasets containing tabular information but use AI and some heuristics to arrive at the solution. A pre-trained model, for example, can predict the pattern that connects the complex structure of a natural language query with tabular data by embedding them and passing through the neural network model. Neural networks help to understand tabular data in multiple ways. The SQL output would then be queried to fetch results. A thesaurus is used to improve keyword filtering to enable easier translation of specific entities like column names, SQL keywords, etc. A set of parsers are used to break down the natural language construct into its respective SQL components. LN2SQL is one approach which used heuristic approaches to convert natural language to SQL queries. A more universal solution is to query the tabular data using natural language. While Structured Query Language (SQL) queries are often used to extract particular information from a database, an individual's knowledge of SQL queries may be limited. Tabular data can also be found in relational databases. Since the data can be big in size, it is not easy to manually extract the answer to a particular user query, given a condition or a set of conditions. Tabular data is data that is structured into rows and columns. Understanding Tabular Data using Heuristic Methods
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