AskChem：以主張為中心的化學文獻綜合基礎設施

摘要

化學文獻的綜合整理往往需要彙整分散在眾多出版物中的具體發現，然而現有的文獻檢索系統主要回傳按相關性排序的文件列表。因此，科學家和 AI 智能體必須自行定位相關資訊、驗證其來源，並手動彙整跨論文的答案。我們提出 AskChem，這是一個以主張為核心的跨論文化學檢索基礎設施。AskChem 將檢索單位從論文轉變為帶有出處的主張：每篇論文被轉換成原子化、類型化的主張，每個主張以來源 DOI 與逐字引用或明確的證據定位器為依據。在此共享主張庫之上，AskChem 提供了互補的檢索與綜合結構：供層級檢索與瀏覽使用的穩定化分面分類法、透過關係連接主張的證據圖譜，以及將已索引論文置於科學原則之下的探索性動態分類法。AskChem 目前索引了來自 14.7 萬篇論文的 240 萬條主張，並提供網頁介面，以及供 AI 智能體使用的 REST、SDK 與 MCP 存取介面。在 AskChem-Bench 上，以 AskChem 作為 GPT-5.5 閱讀器的檢索依據，可解析的 DOI 比例達 100%（無檢索時為 88.3%），並在五個受測系統中取得最高的引用密度。AskChem 已上線，網址為 https://askchem.org。

English

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.