Latent Semantic Search and Information Extraction Architecture

TL;DR

This paper proposes an autonomous, open-source latent semantic search architecture designed to enhance search accuracy, privacy, and offline capabilities, aligned with AGI principles for web search in artificial agents.

Contribution

It introduces a novel architecture for latent semantic search that incorporates AGI principles, enabling autonomous, resource-constrained web search for artificial agents.

Findings

Improved search accuracy and response time.

Enhanced privacy and offline search capabilities.

Open source platform adaptable for various solutions.

Abstract

The motivation, concept, design and implementation of latent semantic search for search engines have limited semantic search, entity extraction and property attribution features, have insufficient accuracy and response time of latent search, may impose privacy concerns and the search results are unavailable in offline mode for robotic search operations. The alternative suggestion involves autonomous search engine with adaptive storage consumption, configurable search scope and latent search response time with built-in options for entity extraction and property attribution available as open source platform for mobile, desktop and server solutions. The suggested architecture attempts to implement artificial general intelligence (AGI) principles as long as autonomous behaviour constrained by limited resources is concerned, and it is applied for specific task of enabling Web search for artificial agents implementing the AGI.