Knowledge Graphs: Technical Construction, Cross-Domain Applications and Future Challenges

The Knowledge Graph has become a crucial technology in the field of Artificial Intelligence, structurally representing real-world entities and their semantic relationships through the Semantic Web. The utilization of knowledge graphs has been demonstrated to facilitate the enhancement of semantic understanding and knowledge extraction for computers. Despite the wide applicability of knowledge bases, there are still challenges in dealing with complex knowledge types and the accuracy of large-scale knowledge bases. This paper systematically reviews the fundamental concepts, construction methodologies, and representative application scenarios of knowledge graphs, including recommender systems, question answering systems, and educational systems. Furthermore, it examines how knowledge graphs are developing right now and considers possible future study avenues based on previous findings. By synthesizing theoretical foundations with practical perspectives, this paper aims to facilitate deeper understanding, effective implementation, and continuous innovation in knowledge graph technologies across a wide range of domains. This research can provide both theoretical foundations and practical insights for researchers and practitioners seeking to understand, implement, or advance knowledge graph technologies across various domains.