Papers by Huiwen Yang

2 papers
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

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Challenge: Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach: They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome: The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.
Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision (2026.acl-long)

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Challenge: Existing methods for integrating textual graphs with LLMs are limited by symbolic inference and high annotation costs.
Approach: They propose a textual graph reasoning framework that integrates textual diagrams with large language models.
Outcome: The proposed approach achieves 15.6% accuracy and 17.2% in F1 score on three common datasets.

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