Skip to main content
Log in

The Blueprint of Data Intelligence Based on Factor Space Theory

  • Published:
Annals of Data Science Aims and scope Submit manuscript

Abstract

Data intelligence is the core task of the information revolution entering the Internet era. It brings opportunities, but also makes human civilization face risks. Data drowns the idea and data is supreme. People regard manufacturing data as the goal of digital economy, stook data up hoarding and turn data into an immortal holy thing, which is very harmful. This paper insists on leading the data with thinking, and puts forward the blueprint of constructing a huge knowledge base with factor pedigree and factor encoding. Factor pedigree is an embedded high-level knowledge graph. Factor encoding is a program for organizing concepts according to connotation. It can not only prevent the proliferation of data, but also be of great significance for natural language understanding.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 5.1
Table 5.1
Table 5.2
Fig. 5.2
Fig. 3

Similar content being viewed by others

References

  1. Zhong YX (2014) The Principle of Higher Artificial Intelligence: Idea, Method and Modle. Science Press, Beijing

    Google Scholar 

  2. He HC (2001) Principles of Universal logic. Science press, Beijing

    Google Scholar 

  3. Zhong YX (2011) Machanism Theory of Artificial Intelligence. BUPT Beijing Press, Beijing

    Google Scholar 

  4. He HC, Zhou JC, Zhou YQ (2011) Universal logic of Propositoins and. Soft Neurons, Beijing: BUPT Beijing Press,

    Google Scholar 

  5. Wang PZ, Liu HT (2011) Factor Space and Artificial Intelligence. BUPT Beijing Press, Beijing

    Google Scholar 

  6. Pujara J, Miao II (2013) L. Getoor. Knowledge graph identification [C]. Proceedings of the International Semantic Web Conference, : 542–557

  7. Wille R (1982) Restructuring lattice theory: An approach based on hierarchies of concepts [J]. Ordered Set, :445–470

  8. Pawlak Z (1982) Rough sets [J].International Journal of Computer and Information Sciences, (11):341–356

  9. Wang PZ, (1982) Sugeno M Factors field and the background structure of fuzzy sets, Fuzzy Mathematics, (2):45–54

  10. Shi Y Big digital economy data analysis and factor space, Special Speech at 5th Intellligence Science and Mathematics Forum, July 24–25, Fuxin, China

  11. Meng XF Factor Query Language (FQL) – A Fundamental Language for the Next Generation of Intelligent Database (Key Speech2021 Intelligence Science and Math Forum, July, 24–25,2021, Fuxin, China

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to PeiZhuang Wang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, P., Li, H., Ouyang, H. et al. The Blueprint of Data Intelligence Based on Factor Space Theory. Ann. Data. Sci. 9, 431–448 (2022). https://doi.org/10.1007/s40745-022-00402-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40745-022-00402-y

Keywords

Navigation