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 …
A comprehensive overview of knowledge graph completion
T Shen, F Zhang, J Cheng - Knowledge-Based Systems, 2022 - Elsevier
Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …
downstream knowledge-aware tasks (such as recommendation and intelligent question …
Knowledge graph embedding for link prediction: A comparative analysis
Knowledge Graphs (KGs) have found many applications in industrial and in academic
settings, which in turn, have motivated considerable research efforts towards large-scale …
settings, which in turn, have motivated considerable research efforts towards large-scale …
[图书][B] Graph representation learning
WL Hamilton - 2020 - books.google.com
This book is a foundational guide to graph representation learning, including state-of-the art
advances, and introduces the highly successful graph neural network (GNN) formalism …
advances, and introduces the highly successful graph neural network (GNN) formalism …
Tucker: Tensor factorization for knowledge graph completion
Knowledge graphs are structured representations of real world facts. However, they typically
contain only a small subset of all possible facts. Link prediction is a task of inferring missing …
contain only a small subset of all possible facts. Link prediction is a task of inferring missing …
Rotate: Knowledge graph embedding by relational rotation in complex space
We study the problem of learning representations of entities and relations in knowledge
graphs for predicting missing links. The success of such a task heavily relies on the ability of …
graphs for predicting missing links. The success of such a task heavily relies on the ability of …
Inductive relation prediction by subgraph reasoning
The dominant paradigm for relation prediction in knowledge graphs involves learning and
operating on latent representations (ie, embeddings) of entities and relations. However …
operating on latent representations (ie, embeddings) of entities and relations. However …
End-to-end structure-aware convolutional networks for knowledge base completion
Abstract Knowledge graph embedding has been an active research topic for knowledge
base completion, with progressive improvement from the initial TransE, TransH, DistMult et …
base completion, with progressive improvement from the initial TransE, TransH, DistMult et …
A survey on knowledge graph embeddings for link prediction
M Wang, L Qiu, X Wang - Symmetry, 2021 - mdpi.com
Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …
in information retrieval, natural language processing, recommendation systems, etc …
Knowledge graph embedding: A survey of approaches and applications
Knowledge graph (KG) embedding is to embed components of a KG including entities and
relations into continuous vector spaces, so as to simplify the manipulation while preserving …
relations into continuous vector spaces, so as to simplify the manipulation while preserving …