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I watched this lecture by professor Xin Wang, and a picture in the beginning interested me:

enter image description here

The confusing thing is that this lecture was delivered on Sep. 19, 2022, but it seems from the diagram above that over the last ten years knowledge graph has made no progress. The diagram is still what I was shown when I was a student in 2015 or 2016.

Since I cannot get a hold of this professor, I googled knowledge graph progress pictures but found that no single one mentioned any big events after 2012.

What happened over the last decade to knowledge graph? Any big events?

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This 2021 survey of Knowledge Graphs depicts a similar chart (appendix A), with the same 2012 final milestone.

That said, a major advancement is the use deep learning to encode the knowledge graph, to extract relations and autonomously complete graphs. So we may expect soon a breaktrough in neural symbolic approaches applied to challenges such as Question Answering, Recommender Systems and Language Representation Learning.

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Knowledge harvesting is the key progress.

This formerly elusive vision has become practically viable today, made possible by major advances in knowledge harvesting. This comprises methods for turning noisy Internet content into crisp knowledge structures on entities and relations. Knowledge harvesting methods have enabled the automatic construction of knowledge bases (KB): collections of machine-readable facts about the real world. Today, publicly available KBs provide millions of entities (such as people, organizations, locations and creative works like books, music etc.) and billions of statements about them (such as who founded which company when and where, or which singer performed which song). Proprietary KBs deployed at major companies comprise knowledge at an even larger scale, with one or two orders of magnitude more entities.

References:

  1. Machine knowledge: Creation and curation of comprehensive knowledge bases
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