Knowledge Graph Embedding: A Survey of Approaches and Applications

By Quan Wang, Zhendong Mao, Bin Wang, Li Guo. 2017

2,573 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Representation & Self-Supervised Learning, Knowledge Graphs & Representation

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Citations in the corpus

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022, background): “Each edge is represented as a triple of the form (head entity, relation, tail entity), also called a fact, indicating that two entities are connected by a specific relation.(Wang et al., 2017)”
Knowledge Graph Convolutional Networks for Recommender Systems (2019, methodology): “pite the above benefits, utilizing KG in RS is rather challenging due to its high dimensionality and heterogeneity. One feasible way is to preprocess the KG by knowledge graph embedding (KGE) methods [20], which map entities and relations to low-dimensional representation vectors [9, 19, 23]. Howeve”
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018, methodology): “k also connects to a large body of work in KGE methods. KGE intends to embed entities and relations in a KG into continuous vector spaces while preserving its inherent structure. Readers can refer to [30] for a more comprehensive survey. KGE methods are mainly classified into two categories: (1) tra”
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “, [11] mainly introduces twelve representative graph embedding algorithms, and [13] focuses on knowledge graph embedding only.”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, neutral): “embed components of a knowledge graph including entities and relations into continuous vector spaces, so as to simplify the manipulation while preserving the inherent structure of the knowledge graph [26].”
Composition-based Multi-Relational Graph Convolutional Networks (2019, background): “Knowledge Graph Embedding: Knowledge graph (KG) embedding is a widely studied ﬁeld (Nickel et al., 2016; Wang et al., 2017) with application in tasks like link prediction and question answering (Bordes et al., 2014).”
Deep learning for noise-tolerant RDFS reasoning (2019, background): “This review is partially based on three recent surveys of graph embedding techniques and their applications [4, 39, 40].”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “Knowledge graphs, being a popular way to instantiate ontology, are commonly used to connect concepts in ontologies with specific data for knowledge engineering and information retrieval (Wang et al., 2017; Lin et al., 2015).”
DEBAGREEMENT: A comment-reply dataset for (dis)agreement detection in online debates (2021, background): “[10] reviews how structured knowledge, such as knowledge graph (KG) embeddings [40], have been combined with language models.”
Multi-modal Knowledge Graphs for Recommender Systems (2020, methodology): “And then entity-based methods use CNN-based KGE method to train the knowledge graph embedding.”
A Survey of the Usages of Deep Learning for Natural Language Processing (2020, background): “Graph embedding is an active area of research [161], and work on integrating language-based models and graph models has only recently begun to take off, giving hope for better machine understanding.”
Adaptive Convolution for Multi-Relational Learning (2019, background): “See (Wang et al., 2017) for a recent survey.”
Knowledge Graph Embedding Based Question Answering (2019, background): “A knowledge graph (KG) is a directed graph with real-world entities as nodes and their relations as edges [25, 36].”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “Note that there are already many good surveys on static graphs [88, 251, 27, 48, 172, 227].”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “into continuous vector spaces such that the reasoning can be done by leveraging translational distance models and semantic matching models [223].”
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions (2019, background): “A survey of all variants of existing KG embedding techniques can be found in (Nickel et al. 2016; Wang et al. 2017).”
Traffic Flow Prediction via Spatial Temporal Graph Neural Network (2020, background): “It aims to learn low-dimensional distributed representations for network nodes which then can be used in a wide variety of applications, such as node classification [13], community detection [23], link prediction [37], recommendation [18], knowledge graph [41], and so on.”
Knowledge Graphs (2020, neutral): “The use of the tensorW results in a high number of parameters, which limits scalability [548].”
Knowledge Graph Embedding for Link Prediction (2020, background): “[63] and by Nguyen [40] are the most relevant to our work, as they specically focus on KG Embedding methods.”