Exploratory Search Prompt Generation using n-Degree Connection in Knowledge Graph

Search engines play a vital role in retrieving in- formation, but users often struggle to express their precise information needs, resulting in less-than-optimal search results. Therefore, enhancing search query refinement is crucial to elevate the accuracy and relevance of search outcomes. One particular challenge that existing search engines face is presenting refined results for queries containing two or more unrelated entities.

This paper presents a novel approach for efficient search prompt generation by leveraging connected nodes and attributes in the knowledge graph. We propose a comprehensive exploration technique that explores the n-degrees connections and their at- tributes to generate all possible imaginative prompts. We realized that n-degrees connection exploration is an expensive task, hence we conducted experiments to determine the effectiveness of 2- degree exploration prompts in covering all the user-asked queries within the provided dataset.

In testing with approximately 2000 queries on a related knowl- edge graph dataset, we found out that our proposed methodol- ogy significantly improved query coverage. At n=1 depth, the coverage increased from 58% without the methodology to 84% with it. At n=2 depth, the coverage rose from 92% without the methodology to nearly 99% with it. Additionally, due to our question caching strategy at n=2, we observed faster response times for all questions.