Knowledge fusion patterns: A survey

By A. Smirnov, T. Levashova. 2019

34 citations recorded by the source corpus.

Abstract The survey analyzes, summarizes, and classifies knowledge fusion patterns reported in different studies. It provides overview of the patterns explicitly declared in the found studies and proposes some patterns that have been recognized as such. Namely, some knowledge fusion models are considered as patterns. The patterns are classified from the perspective of the types of resources that provide knowledge for the fusion process. Two types of resources are distinguished: knowledge worker and knowledge repository. Three groups of patterns are differentiated: (1) patterns fusing knowledge stored in knowledge repositories; (2) patterns fusing knowledge of knowledge workers; and (3) patterns fusing knowledge of knowledge workers and knowledge accumulated in repositories. The patterns are specified in terms of the pattern names, the inputs and outputs, the methods supporting knowledge fusion, and the references to the sources introducing these patterns.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems, Knowledge Graphs & Representation

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. Ciyuan Peng
2. Francesco Osborne
3. Feng Xia
4. Mehdi Naseriparsa

In Computational Theory and Mathematics

1. Ciyuan Peng
2. Francesco Osborne
3. Feng Xia
4. Mehdi Naseriparsa

Citations in the corpus

• Knowledge Graphs: Opportunities and Challenges (2023, background): “It is often a necessary step for the generation of knowledge graphs (Nguyen et al. 2020; Smirnov and Levashova 2019).”