A Survey on Application of Knowledge Graph

By Xiaohan Zou. Journal of Physics, Conference Series, 2020

319 citations recorded by the source corpus.

Knowledge graphs, representation of information as a semantic graph, have caused wide concern in both industrial and academic world. Their property of providing semantically structured information has brought important possible solutions for many tasks including question answering, recommendation and information retrieval, and is considered to offer great promise for building more intelligent machines by many researchers. Although knowledge graphs have already supported multiple “Big Data” applications in all sorts of commercial and scientific domains since Google coined this term in 2012, there was no previous study give a systemically review of the application of knowledge graphs. Therefore, unlike other related work which focuses on the construction techniques of knowledge graphs, this present paper aims at providing a first survey on these applications stemming from different domains. This paper also points out that while important advancements of applying knowledge graphs’ great ability of providing semantically structured information into specific domains have been made in recent years, several aspects still remain to be explored.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e5e605a4677a4c02d1fdf8e29e66aef60411d27a

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Citations in the corpus

Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning (2021, background): “Knowledge Graphs (KGs) have facilitated many real-world applications [44].”
Knowledge Graphs: Opportunities and Challenges (2023, background): “Specifically, there are a couple of systems, including recommender systems, question-answering systems, and information retrieval tools (Guo et al. 2020; Zou 2020), which utilize knowledge graphs for their input data and benefit the most from knowledge graphs.”
Asymptotically perfect seeded graph matching without edge correlation (and applications to inference) (2025, unknown): “Graph matching is a broad term for the large collection of methods that are dedicated to finding an optimal alignment between the vertex sets of two (or more) networks; see the survey papers [23], [49], [50] for a review of the field.”