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.

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

In this paper we provide a comprehensive introduction to knowledge graphs,
which have recently garnered significant attention from both industry and
academia in scenarios that require exploiting diverse, dynamic, large-scale
collections of data. After some opening remarks, we motivate and contrast
various graph-based data models and query languages that are used for knowledge
graphs. We discuss the roles of schema, identity, and context in knowledge
graphs. We explain how knowledge can be represented and extracted using a
combination of deductive and inductive techniques. We summarise methods for the
creation, enrichment, quality assessment, refinement, and publication of
knowledge graphs. We provide an overview of prominent open knowledge graphs and
enterprise knowledge graphs, their applications, and how they use the
aforementioned techniques. We conclude with high-level future research
directions for knowledge graphs.

Knowledge Graphs

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. 

/pdf/a-survey-on-knowledge-graphs-representation-acquisition-and-2el2qizl.pdf

A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

A knowledge graph is a kind of semantic network representing some scientific theory. The article describes the present state of this field and addresses a number of problems that have not yet been solved. These problems are implicit relations, strength of (causal) relations, and exclusiveness. Concepts might be too broad or complex to be used properly, so directions for solving these problems are explored. The solutions are applied to a knowledge graph in the field of labour markets.

/pdf/knowledge-graphs-and-network-text-analysis-48vp9hoh7x.pdf

Knowledge graphs and network text analysis

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation.

Knowledge Engineering with Big Data

The idea that knowledge is a commodity that can be used by machines in various ways forms the entry to our discussion of knowledge based systems. We will limit ourselves to those systems that are in an active dialogue with their human user. As a consequence, the use and structuring of knowledge in autonomous systems, like robots or visual pattern recognizers, will not be discussed. In general, knowledge-based systems cannot be considered as models of human cognition. Rather, they should provide an extension of the user’s cognitive abilities.

/pdf/structuring-knowledge-in-a-graph-339mq0xdiu.pdf

Pieter H. de Vries

Papers

Structuring knowledge in a graph

Chapter 11. Structuring Knowledge in a Graph