We live in an era of information explosion, where new data is generated constantly. Crude oil is refined into gasoline, diesel, and chemical products, which are higher-value commodities. Similarly, data is refined and processed into information, knowledge, and wisdom to unleash greater value. So how do we extract value from data?
The DIKW (Data, Information, Knowledge, Wisdom) framework answers this question by organizing data, information, knowledge, and wisdom into a pyramid structure. Raw observations and measurements yield data, analyzing the relationships between data produces information, applying information leads to knowledge, and knowledge further guides decision-making, forming wisdom, enabling both humans and machines to predict the future.
Knowledge Graph technology, one of the most discussed artificial intelligence technologies in recent years, is a critical link in the DIKW system. It efficiently transforms data and information into knowledge, further aiding the generation of wisdom.
Gartner, an international authoritative information technology research and analysis consulting firm, selects over 30 hot sub-technologies in the field of artificial intelligence each year, illustrating the five key stages of emerging technologies through a maturity curve: the technology trigger phase, the peak of inflated expectations, the trough of disillusionment, the slope of enlightenment, and the plateau of productivity. This analysis reveals the maturity of these sub-technologies in the global market and their future development trends. This analysis is currently recognized as the bellwether of artificial intelligence worldwide.
▲ AI Technology Maturity Curve
In Gartner’s 2020 AI technology maturity curve, the Knowledge Graph is almost at the peak of the curve. In fact, Knowledge Graphs were not included in the AI technology maturity curve in 2017, were near the origin in 2018, became a hot topic at the World Artificial Intelligence Conference in 2019, and by 2020, Knowledge Graphs had become one of the key technologies.
The Knowledge Graph was initially proposed by Google around 2012, primarily to optimize search engines. Over time, its applications have expanded to more scenarios, such as voice assistants, chatbots, and intelligent Q&A systems, and later applied in finance, government, and healthcare sectors. Even after 10 years, as one of the core technologies in artificial intelligence, the popularity of Knowledge Graph technology remains undiminished.
The book “Knowledge Graph” not only covers the introduction of core concepts, explanations of key technologies, and sharing of typical cases but also includes hands-on experimental activities designed to encourage readers to unleash their unique imagination and design their own Knowledge Graph projects. I believe you will enjoy the endless fun of creation during this process.
Knowledge Graph Display
▲ Semantic Web Architecture
▲ Knowledge Management Trends of Knowledge Graphs in the Digital Age
▲ Cross-Language Alignment
Artificial Intelligence and Intelligent Education Series
Yuan Zhenguo, Chief Editor
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PS: I wonder if everyone has noticed that WeChat has recently changed its push rules, no longer sorting by chronological order.
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