Knowledge graphs (KGs) offer a structured

machine-readable format for organizing complex information. In heterogeneous catalysis

where data on catalytic materials

reaction conditions

mechanisms

and synthesis routes are dispersed across diverse sources

KGs provide a semantic framework that supports data integration under the FAIR (Findable

Accessible

Interoperable

and Reusable) principles. This review aims to survey recent developments in catalysis KGs

describe the main techniques for graph construction

and highlight how artificial intelligence

particularly large language models (LLMs)

enhances graph generation and query. We conducted a systematic analysis of the literature

focusing on ontology-guided text mining pipelines

graph population methods

and maintenance strategies. Our review identifies key trends: ontology-based approaches enable the automated extraction of domain knowledge

LLM-driven retrieval-augmented generation supports natural-language queries

and scalable graph architectures range from a few thousand to over a million triples. We discuss state-of-the-art applications

such as catalyst recommendation systems and reaction mechanism discovery tools

and examine the major challenges

including data heterogeneity

ontology alignment

and long-term graph curation. We conclude that KGs

when combined with AI methods

hold significant promise for accelerating catalyst discovery and knowledge management

but progress depends on establishing community standards for ontology development and maintenance. This review provides a roadmap for researchers seeking to leverage KGs to advance heterogeneous catalysis research.