A Survey on Knowledge Graphs: Representation, Acquisition and Applications
TLDR
A comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research.Abstract:
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.read more
Citations
More filters
Journal ArticleDOI
Knowledge Graphs
Aidan Hogan,Eva Blomqvist,Michael Cochez,Claudia d'Amato,Gerard de Melo,Claudio Gutierrez,José Emilio Labra Gayo,Sabrina Kirrane,Sebastian Neumaier,Axel Polleres,Roberto Navigli,Axel-Cyrille Ngonga Ngomo,Sabbir M. Rashid,Anisa Rula,Lukas Schmelzeisen,Juan F. Sequeda,Steffen Staab,Antoine Zimmermann +17 more
TL;DR: The historical events that lead to the interweaving of data and knowledge are tracked to help improve knowledge and understanding of the world around us.
Journal ArticleDOI
Text Data Augmentation for Deep Learning.
TL;DR: A survey of data augmentation for text data can be found in this article, where the major motifs of Data Augmentation are summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form.
Journal ArticleDOI
Domain-specific knowledge graphs: A survey
TL;DR: This survey is the first to provide an inclusive definition to the notion of domain KG, and a comprehensive review of the state-of-the-art approaches drawn from academic works relevant to seven dissimilar domains of knowledge is provided.
Journal ArticleDOI
A survey on empathetic dialogue systems
TL;DR: This review article focuses on the literature of empathetic dialogue systems, whose goal is to enhance the perception and expression of emotional states, personal preference, and knowledge, and identifies three key features that underpin such systems: emotion-awareness, personality-awareness and knowledge-accessibility.
Journal ArticleDOI
Graph Learning: A Survey
TL;DR: A comprehensive overview on the state-of-the-art of graph learning can be found in this paper, where four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning are reviewed.
References
More filters
Journal ArticleDOI
The Protein Data Bank
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Posted Content
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
Journal ArticleDOI
WordNet: a lexical database for English
TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.
Proceedings Article
Translating Embeddings for Modeling Multi-relational Data
TL;DR: TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases.
Book ChapterDOI
DBpedia: a nucleus for a web of open data
TL;DR: The extraction of the DBpedia datasets is described, and how the resulting information is published on the Web for human-andmachine-consumption and how DBpedia could serve as a nucleus for an emerging Web of open data.