Knowledge Graphs 2021: A Data Odyssey
By Gerhard Weikum. 2021
48 citations recorded by the source corpus.
Providing machines with comprehensive knowledge of the world’s entities and their relationships has been a long-standing vision and challenge for AI. Over the last 15 years, huge knowledge bases, also known as knowledge graphs, have been automatically constructed from web data, and have become a key asset for search engines and other use cases. Machine knowledge can be harnessed to semantically interpret texts in news, social media and web tables, contributing to question answering, natural language processing and data analytics. This position paper reviews these advances and discusses lessons learned. It highlights the role of "DB thinking" in building and maintaining high-quality knowledge bases from web contents. Moreover, the paper identifies open challenges and new research opportunities. In particular, extracting quantitative measures of entities (e
Subfields: Artificial Intelligence, Management Science and Operations Research, 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
- David Bau
- Alex Andonian
- Kevin Meng
- Yonatan Belinkov
- Arnab Sen Sharma
- Max Tegmark
- Percy Liang
- Efstratios Gavves
In Management Science and Operations Research
In Knowledge Graphs & Representation
Citations in the corpus
- Mass-Editing Memory in a Transformer (2022, background): “, 1993), but they are limited by sparse coverage of uncatalogued knowledge, such as commonsense facts (Weikum, 2021).”