Knowledge Graph Construction

Knowledge graph construction (KGC) aims to automatically create structured representations of information from unstructured data sources, primarily text, to facilitate knowledge discovery and reasoning. Current research heavily utilizes large language models (LLMs) within various architectures, including those employing zero-shot learning, retrieval-augmented generation, and multi-agent collaborations, to improve entity and relation extraction, schema generation, and overall graph quality. This field is significant because automatically building comprehensive and accurate knowledge graphs enables advancements in diverse applications, such as improved search, recommendation systems, scientific discovery, and even assisting in tasks like radiology report generation and water footprint reduction. The focus is on developing robust, scalable, and accurate methods for KGC, often incorporating human-in-the-loop validation to ensure quality and address challenges like handling inconsistencies and resolving ambiguities.