Journal Article10.1109/TKDE.2017.2754499
Knowledge Graph Embedding: A Survey of Approaches and Applications
2.8K
TL;DR: This article provides a systematic review of existing techniques of Knowledge graph embedding, including not only the state-of-the-arts but also those with latest trends, based on the type of information used in the embedding task.
read more
Abstract: 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 the inherent structure of the KG. It can benefit a variety of downstream tasks such as KG completion and relation extraction, and hence has quickly gained massive attention. In this article, we provide a systematic review of existing techniques, including not only the state-of-the-arts but also those with latest trends. Particularly, we make the review based on the type of information used in the embedding task. Techniques that conduct embedding using only facts observed in the KG are first introduced. We describe the overall framework, specific model design, typical training procedures, as well as pros and cons of such techniques. After that, we discuss techniques that further incorporate additional information besides facts. We focus specifically on the use of entity types, relation paths, textual descriptions, and logical rules. Finally, we briefly introduce how KG embedding can be applied to and benefit a wide variety of downstream tasks such as KG completion, relation extraction, question answering, and so forth.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations
TL;DR: In this article, the authors consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph and propose a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings.
9
•Posted Content
Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction Perspective
Hung Nghiep Tran,Atsuhiro Takasu +1 more
TL;DR: In this article, a multi-embedding interaction mechanism is proposed to unify and generalize these models and derive them theoretically via this mechanism and provide empirical analyses and comparisons between them.
9
•Posted Content
Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings.
TL;DR: In this article, the authors developed two lightweight Euclidean-based models, called RotL and Rot2L, which simplifies the hyperbolic operations while keeping the flexible normalization effect.
9
Commonsense Knowledge Graph towards Super APP and Its Applications in Alipay
Xiaoling Zang,Binbin Hu,Jun Chu,Zhiqiang Zhang,Guannan Zhang,Jun Zhou,Wenliang Zhong +6 more
- 04 Aug 2023
TL;DR: A novel knowledge graph representation learning framework is developed for SupKG, enabling various downstream applications to benefit from learned representations of entities and relations, and shows the potential superiority of integrating global behaviors in cold-start scenarios and providing high-quality knowledge for warming up the graph-based ranking.
9
Hybrid attention-based transformer block model for distant supervision relation extraction
TL;DR: Zhang et al. as mentioned in this paper proposed a new framework using hybrid attention-based Transformer block with multi-instance learning for distant supervision relation extraction, which mainly utilizes multi-head self-attention to capture syntactic information at the word level.
References
•Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov,Ilya Sutskever,Kai Chen,Greg S. Corrado,Jeffrey Dean +4 more
- 05 Dec 2013
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Tensor Decompositions and Applications
Tamara G. Kolda,Brett W. Bader +1 more
TL;DR: This survey provides an overview of higher-order tensor decompositions, their applications, and available software.
DeepWalk: online learning of social representations
Bryan Perozzi,Rami Al-Rfou,Steven Skiena +2 more
- 24 Aug 2014
TL;DR: DeepWalk as mentioned in this paper uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences, which encode social relations in a continuous vector space, which is easily exploited by statistical models.