Recently, I have received many letters from readers, most of whom are just starting to explore the fields of knowledge graphs and natural language processing, and the unfamiliarity brings some insecurity, leaving them feeling a bit lost. Therefore, taking this opportunity, this article discusses the topic of “How to Get Started with Knowledge Graphs and … Read more
What Is Bibliometrics? Bibliometrics quantitatively reveals the development history, research hotspots, and development directions of a certain academic field. What Is the Use of Bibliometrics? When selecting topics and writing literature reviews, bibliometric analysis can help you quickly locate key literature, prolific authors or institutions, and current research hotspots from a vast amount of literature, … Read more
The research hotspot of knowledge graphs has gradually shown a tendency to emphasize quantity over structuredness, which is closely related to the prevalence of deep learning and connectionist ideas. Cognitive graphs dynamically construct knowledge graphs with contextual information based on the dual-processing theory of human cognition and perform reasoning. This article reviews the historical development … Read more
This article is approximately 9200 words long and is recommended for a reading time of over 10 minutes. This article mainly shares typical cases in the financial sector and further reflects on common issues in the implementation of large models in vertical domains. Introduction The large model from Hang Seng Electronics has been implemented in … Read more
This article is approximately 5500 words long and is recommended for an 11-minute read. This paper proposes a pseudo-graph structure by relaxing the pattern constraints on data and relationships in traditional KGs. Paper Title: Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning Author Affiliation: Renmin University of China (RUC), Shanghai … Read more
Source: DeepHub IMBA This article is approximately 4600 words long and is recommended to be read in 10 minutes. In this article, we will combine text and knowledge graphs to enhance the performance of our RAG. In our previous articles, we introduced examples of combining knowledge graphs with RAG. In this article, we will combine … Read more
Source: DeepHub IMBA This article is about 1600 words long and is recommended to be read in 5 minutes. Retrieval-Augmented Generation is a technical approach aimed at enhancing the performance of large language models. Overview of Retrieval-Augmented Generation (RAG) Technology Retrieval-Augmented Generation (RAG) is a technical method aimed at enhancing the performance of Large Language … Read more
Abstract: The transition from traditional RAG to Graph RAG enhances large language models by integrating knowledge graphs, enabling them to provide more detailed and accurate responses to complex queries. The effectiveness of Graph RAG also depends on the quality and breadth of the underlying knowledge graph and the engineering aspects of RAG. Main Points: – … Read more
Machine Heart Reprint Authors:Han Xu, Gao Tianyu, Liu Zhiyuan This article is written by Professor Liu Zhiyuan from Tsinghua University and his students Han Xu and Gao Tianyu, introducing the topic of knowledge graphs. The article reviews the development of the knowledge graph field and summarizes recent research progress. It has been authorized for reprint … Read more
With the development of artificial intelligence research, knowledge graphs (KGs) have attracted wide attention from both academia and industry. As a representation of semantic relationships between entities, knowledge graphs play an important role in natural language processing (NLP) and have seen rapid promotion and widespread adoption in recent years. Given the increasing workload of research … Read more