A New Neural Search and Insights Platform for Navigating and Organizing AI Research

TL;DR

The paper introduces AI Research Navigator, a platform combining neural retrieval and classical search to help AI researchers explore and organize vast literature using multi-level search and a knowledge graph.

Contribution

It presents a novel integrated platform that combines neural and keyword search with knowledge graph navigation for AI research literature.

Findings

Enhanced literature discovery through multi-level search.

Integration of neural retrieval with domain-specific knowledge graph.

System architecture supporting diverse research needs.

Abstract

To provide AI researchers with modern tools for dealing with the explosive growth of the research literature in their field, we introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. The system provides search at multiple levels of textual granularity, from sentences to aggregations across documents, both in natural language and through navigation in a domain-specific Knowledge Graph. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.