# Data Imputation Based on Retrieval-Augmented Generation
## 3 Methods
### Semantic Data Lake Indexing Technology  
The presence of massive heterogeneous data in data lakes poses significant challenges for data retrieval and integration. To enable efficient and accurate retrieval-augmented methods, we propose a unified indexing approach for heterogeneous data based on feature extraction and joint representation learning, as illustrated in Figure 2.  
**Representation and Chunking of Heterogeneous Data.** In the feature extraction stage, unstructured data--such as text--can be directly represented in a structured format using bag-of-words techniques without relying on external knowledge. Although this process unifies the representation format of features, the resulting vector spaces for different data types remain distinct, making it difficult to construct effective indexes. Therefore, it is necessary to map both structured and unstructured data from their respective representation spaces into a shared vector space during the joint representation learning stage. To  
Figure 1: Overall framework.  
achieve this, we employ contrastive learning to construct the joint representation model, ensuring that semantically related heterogeneous data are embedded closer together in the new vector space.  
**Unified Representation Based on Contrastive Learning.** Contrastive learning plays a pivotal role in this process. Its core objective is to achieve semantic alignment in the representation space by minimizing the distance between semantically similar sample pairs while maximizing the distance between dissimilar ones during training. Specifically, given a sample pair \((x_{i},x_{j})\), the goal of contrastive learning is to optimize the following loss function:  
\[L=-\log\bigg{(}\frac{\exp(\text{sim}(x,x^{+}))}{\exp(\text{sim}(x,x^{+}))+ \sum_{x^{-}}\exp(\text{sim}(x,x^{-}))}\bigg{)} \tag{1}\]  
where \(x\) represents the current training data, \(x^{+}\) is a positive example, \(x^{-}\) is a negative example, and \(\text{sim}(x,x^{+})\) and \(\text{sim}(x,x^{-})\) are similarity functions between the pairs. This formulation aims to optimize the training process by minimizing the similarity between positive examples while maximizing the similarity between negative examples. Additionally, since contrastive learning lacks labeled training samples, innovative solutions are required, such as keyword-based indexing and semantic vector nearest neighbor graph indexing, to infer correlations and construct high-quality labeled training sets.  
To more accurately distinguish semantically related and unrelated data points during the training phase, the framework adopts a Siamese network architecture consisting of dual encoders with shared weights. This design enables the model to more effectively capture semantic differences between relevant and irrelevant training samples.  
**Construction of Semantically Based Indexes.** Effective indexing is paramount in terms of enabling rapid and accurate data retrieval. Once the heterogeneous data has been uniformly characterized and embedded into the unified vector space, semantically based indexes are constructed using vector databases. These indexes facilitate efficient similarity searches, allowing the framework to retrieve semantically relevant data points swiftly. Techniques such as Locality-Sensitive Hashing (LSH) and nearest neighbor graph indexing are integrated to optimize the indexing process, ensuring scalability and performance, even with large-scale datasets.  
**Efficiency in Large-Scale Similarity Searches.** In the context of data lakes containing millions of data points, performing large-scale similarity search efficiently requires unified representation of all data. After standardizing the representations, the trained neural  
Figure 2: Semantic data lake indexing technology.  
network is used to embed the data into a shared vector space, upon which an index is built using a vector database to enable efficient subsequent retrieval.