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Journal ArticleDOI

Knowledge graph refinement: A survey of approaches and evaluation methods

Heiko Paulheim
- 06 Dec 2016 - 
- Vol. 8, Iss: 3, pp 489-508
TLDR
A survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.
Abstract
In the recent years, different Web knowledge graphs, both free and commercial, have been created. While Google coined the term "Knowledge Graph" in 2012, there are also a few openly available knowledge graphs, with DBpedia, YAGO, and Freebase being among the most prominent ones. Those graphs are often constructed from semi-structured knowledge, such as Wikipedia, or harvested from the web with a combination of statistical and linguistic methods. The result are large-scale knowledge graphs that try to make a good trade-off between completeness and correctness. In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces of information. In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.

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Citations
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Knowledge Graph Embedding: A Survey of Approaches and Applications

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.
Journal ArticleDOI

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

TL;DR: 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.
Journal ArticleDOI

Knowledge Graphs

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.
Book ChapterDOI

RDF2Vec: RDF Graph Embeddings for Data Mining

TL;DR: RDF2Vec is presented, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs, and shows that feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.
Journal ArticleDOI

A review: Knowledge reasoning over knowledge graph

TL;DR: The basic concept and definitions of knowledge reasoning and the methods for reasoning over knowledge graphs are reviewed, and the reasoning methods are dissected into three categories: rule- based reasoning, distributed representation-based reasoning and neural network-based Reasoning.
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