Dataset Similarity Estimation Using LLM-Based Metadata Embeddings

To increase the use of research artifacts, such as datasets, it is essential not only to publish them but also to ensure their accessibility. One effective way to enhance accessibility is by revealing relationships between research artifacts, with similarity being a key type of relationship. Identifying the similarities can support the recommendation of research artifacts. A previous study estimated dataset similarity using their metadata. In recent years, methods that estimate sentence similarity through embeddings generated by large language models (LLMs) have achieved strong performance. These findings suggest that LLMs can also be effective for estimating dataset similarity. This paper experimentally verifies the effectiveness of LLMs in estimating similarity between datasets. We implement a similarity estimation method that generates embeddings of dataset metadata using LLMs and computes the cosine similarity between these embeddings. To generate embeddings, we use PromptEOL, which produces high-quality text embeddings by prompting LLMs to capture the meaning of input text in one word. An experiment was conducted to evaluate the estimation performance of the implemented method, and the results demonstrated the effectiveness of the LLM-generated metadata embeddings for estimating dataset similarity