Latent Semantic Search and Information Extraction Architecture
This addresses the problem of enabling efficient and private semantic search for artificial agents in resource-constrained environments, though it appears incremental as it builds on existing latent search concepts.
The paper tackles the limitations of existing latent semantic search engines, such as insufficient accuracy, slow response times, privacy issues, and lack of offline capabilities, by proposing an autonomous search engine architecture with adaptive storage, configurable scope, and built-in entity extraction, applied to enable web search for artificial agents implementing AGI principles.
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.