Since the beginning of the 21st century, with the development of computer technology and the rapid iteration of hardware, human society has successively entered the Internet era, the big data era, and the artificial intelligence era. Especially after entering the second decade of the 21st century, with the resurgence of machine learning, tech giants have … Read more
The development of new generation artificial intelligence technologies such as deep learning, knowledge graphs, and reinforcement learning is driving “Internet + Education” into a new era of “smart education”. As a core driving force for the development of artificial intelligence, knowledge graphs provide new empowering capabilities for education in the era of informationization 2.0. From … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. Source: … Read more
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