Knowledge graphs: Opportunities and challenges
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …
important to organize and represent the enormous volume of knowledge appropriately. As …
Knowledge graphs
In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …
recently garnered significant attention from both industry and academia in scenarios that …
Unifying large language models and knowledge graphs: A roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …
field of natural language processing and artificial intelligence, due to their emergent ability …
Gpt4graph: Can large language models understand graph structured data? an empirical evaluation and benchmarking
Large language models~(LLM) like ChatGPT have become indispensable to artificial
general intelligence~(AGI), demonstrating excellent performance in various natural …
general intelligence~(AGI), demonstrating excellent performance in various natural …
A survey on knowledge graph-based recommender systems
To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …
applications, recommender systems have been developed to model users' preferences …
Learning knowledge graph embedding with heterogeneous relation attention networks
Knowledge graph (KG) embedding aims to study the embedding representation to retain the
inherent structure of KGs. Graph neural networks (GNNs), as an effective graph …
inherent structure of KGs. Graph neural networks (GNNs), as an effective graph …
Sequence-to-sequence knowledge graph completion and question answering
Knowledge graph embedding (KGE) models represent each entity and relation of a
knowledge graph (KG) with low-dimensional embedding vectors. These methods have …
knowledge graph (KG) with low-dimensional embedding vectors. These methods have …
Learning hierarchy-aware knowledge graph embeddings for link prediction
Abstract Knowledge graph embedding, which aims to represent entities and relations as low
dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique …
dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique …
A survey of multi-modal knowledge graphs: Technologies and trends
In recent years, Knowledge Graphs (KGs) have played a crucial role in the development of
advanced knowledge-intensive applications, such as recommender systems and semantic …
advanced knowledge-intensive applications, such as recommender systems and semantic …
A survey of large language models for graphs
Graphs are an essential data structure utilized to represent relationships in real-world
scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver …
scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver …