Knowledge graph refinement: A survey of approaches and evaluation methods

By Heiko Paulheim. Semantic Web, 2016

1,261 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/53f1779c4169b128072e6f50dc3f31bb2c530a70

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xing Xie
2. Philip S. Yu
3. Hui Xiong
4. Shirui Pan
5. Qing He
6. Fuzhen Zhuang
7. Hengshu Zhu
8. Chuan Qin

In Management Science and Operations Research

1. Quan Wang
2. Philip S. Yu
3. Daria Stepanova
4. Xing Xie
5. Hui Xiong
6. Xindong Wu
7. Gerhard Weikum
8. Hao Peng

Citations in the corpus

• A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “refinement [11], Chinese knowledge graph construction [13], knowledge reasoning [14], KGE [5] or KRL [9].”
• ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text (2019, methodology): “However, KGs also contain doubtful if not incorrect SPO triples, as they are partly built by automatic information extraction, crowd-sourcing, or methods for KG completion using rules [15] or embeddings [28, 30, 38].”
• Knowledge Graph Embedding for Link Prediction (2020, background): “This has led researchers to investigate how to identify new facts to add to KGs [54].”
• Knowledge Graphs (2020, neutral): “For further details on knowledge graph refinement, we refer to the survey by Paulheim [399].”
• Towards a Definition of Knowledge Graphs (2016, background): “Other definitions may lead to the assumption that knowledge graph is a synonym for any graph-based knowledge representation (cf. [12] [16]).”
• A Survey on Knowledge Graph-Based Recommender Systems (2020, background): “Though publicly accessible documents about the Satori KG are limited, it is known that Satori consisted of 300 million entities and 800 million relations in 2012 [32].”
• A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, methodology): “[29] provided an encyclopedic survey for the knowledge graph, while Paulheim [30] looks into methods that refine and fill knowledge graphs.”
• Knowledge Graph Embedding: A Survey of Approaches and Applications (2017, background): “, completion or de-duplication) of the input KG [123], from different viewpoints and application context.”