A reading list that maps the knowledge-graph maze

What it does This repository is a curated bibliography and study guide for knowledge graph research. It collects papers, datasets, tools, and reports, then arranges them into a conceptual hierarchy—from high-level surveys down to specific embedding techniques and industrial applications. The maintainer adds brief, opinionated annotations in Chinese that flag classic introductions, distinguish between Baidu and Tsinghua’s respective ERNIE papers, and warn that reasoning techniques are prone to fail in production if forced online.

The interesting bit Unlike a flat awesome-list, the repository structures its collection around a conceptual framework diagram and groups literature by problem layer—representation, acquisition, and application—so readers can locate where a specific technique sits in the larger stack. The commentary is refreshingly blunt, sorting methods into paper-friendly novelties versus techniques with industrial headroom.

Key highlights

• Organizes papers into a top-down concept architecture covering surveys, commonsense reasoning, and KBQA.
• Annotates landmark Chinese-language surveys (e.g., Li Juanzi’s 2017 review) with context on their historical impact.
• Tracks the knowledge-augmented language model thread from early ERNIE variants through KEPLER and K-Adapter.
• Covers practical application areas—dialogue systems, recommendation, and question answering—alongside theoretical embedding work.
• Notes candidly that reasoning is best kept offline and that dialogue systems offer more practical industrial leverage.

Verdict Anyone bootstrapping into knowledge graph research—particularly Chinese-reading students and engineers—will save hours of literature search. If you are looking for runnable code or a novel algorithm implementation, this is a reading list, not a framework.