AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis

2026-07-30

AI Summary

This work addresses the challenge of synthesizing fragmented chemical knowledge from vast scientific literature, a process traditionally reliant on manual curation and poorly supported by conventional document-retrieval systems. To overcome this limitation, the authors propose a novel infrastructure centered on “claims”—structured, traceable assertions—as the fundamental unit of retrieval, shifting from whole-document indexing to fine-grained knowledge representation. The system extracts claims from publications, anchors them to their original sources, classifies them via faceted taxonomies, and organizes them into evidence graphs for dynamic knowledge integration. Deployed over 147,000 papers, it indexes 2.4 million claims, substantially increasing citation density and enabling GPT-5.5 to achieve 100% DOI-resolvability on the AskChem-Bench benchmark. This framework provides an efficient, interpretable interface for both AI agents and researchers to collaboratively explore and validate scientific knowledge.

Abstract

Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems primarily return ranked document lists. As a result, scientists and AI agents need to locate relevant information, verify their provenance, and assemble cross-paper answers manually. We present AskChem, a claim-centered infrastructure for cross-paper chemistry search. AskChem changes the unit of retrieval from the paper to the provenance-carrying claim: each paper is converted into atomic, typed claims, each grounded by a source DOI and a verbatim quote or an explicit evidence locator. Over this shared claim store, AskChem exposes complementary structures for search and synthesis: a stabilized faceted taxonomy for hierarchical retrieval and browsing, an evidence graph linking claims through relations, and an exploratory living taxonomy that situates indexed papers under scientific principles. AskChem currently indexes 2.4M claims from 147K papers and provides a web interface, as well as REST, SDK, and MCP access for AI agents. On AskChem-Bench, grounding a GPT-5.5 reader in AskChem yields 100% resolvable DOIs, compared with 88.3% without retrieval, and the highest citation density among five tested systems. AskChem is live at https://askchem.org.

Problem

• chemistry literature synthesis
• claim-centered retrieval
• cross-paper information integration
• provenance verification
• scientific claim extraction

Innovation

• claim-centered retrieval
• evidence graph
• faceted taxonomy
• literature synthesis
• provenance-grounded claims