By: Ryan Henry, Ryan Henry, Jiaqi Gong
Knowledge graphs (KGs) represent a promising approach for detecting and correcting errors in automated audio and video transcripts. Yet the lack of accessible tools leaves human reviewers with limited support, as KG construction from media data often depends on advanced programming or natural language processing expertise. We present the Custom LLM Automated Relationship Extractor (CLARE), a system that lowers this barrier by combining context-aware relation extraction with an interface for transcript correction and KG refinement. Users import time-synchronized media, correct transcripts through linked playback, and generate an editable, searchable KG from the revised text. CLARE supports over 150 large language models (LLMs) and embedding models, including local options suitable for privacy-sensitive data. We evaluated CLARE on the Measure of Information in Nodes and Edges (MINE) benchmark, which pairs articles with ground-truth facts. With minimal parameter tuning, CLARE achieved 82.1% mean fact accuracy, exceeding Knowledge Graph Generation (KGGen, 64.8%) and Graph Retrieval-Augmented Generation (GraphRAG, 48.3%). We further assessed interactive refinement by revisiting the twenty-five lowest-scoring graphs for fifteen minutes each and found that the fact accuracy rose by an average of 22.7%. These findings show that CLARE both outperforms prior methods and enables efficient user-driven improvements. By streamlining ingestion, correction, and filtering, CLARE makes KG construction more accessible for researchers working with unstructured data.
