Semantica: Decentralized Search using a LLM-Guided Semantic Tree Overlay
Petru Neague, Quinten Stokkink, Naman Goel, Johan Pouwelse

TL;DR
Semantica leverages Large Language Model embeddings to create a decentralized semantic overlay network that improves user connection relevance and document retrieval efficiency, rivaling centralized search engine performance.
Contribution
This paper introduces Semantica, a novel decentralized search algorithm using LLM embeddings to build a semantic trie, enhancing connection accuracy and retrieval speed over existing methods.
Findings
Semantica finds up to ten times more semantically similar users.
It retrieves more than twice the relevant documents under the same network load.
The approach outperforms current state-of-the-art decentralized search methods.
Abstract
Centralized search engines are key for the Internet, but lead to undesirable concentration of power. Decentralized alternatives fail to offer equal document retrieval accuracy and speed. Nevertheless, Semantic Overlay Networks can come close to the performance of centralized solutions when the semantics of documents are properly captured. This work uses embeddings from Large Language Models to capture semantics and fulfill the promise of Semantic Overlay Networks. Our proposed algorithm, called Semantica, constructs a prefix tree (trie) utilizing document embeddings calculated by a language model. Users connect to each other based on the embeddings of their documents, ensuring that semantically similar users are directly linked. Thereby, this construction makes it more likely for user searches to be answered by the users that they are directly connected to, or by the users they are…
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Taxonomy
TopicsSemantic Web and Ontologies · Service-Oriented Architecture and Web Services
