Search is a function that everyone is familiar with now, and it is also an important means that we cannot do without in collecting information. The reason why search can help us obtain the corresponding information is actually inseparable from the important support of knowledge graphs. What is a knowledge graph? Why can it support the improvement of search? Today we will learn about the knowledge graph.
Knowledge graph is a technology that expresses and stores knowledge in the form of graph, which can help machines understand human language and behavior, so as to realize intelligent application.
development path
The development of knowledge graph can be traced back to the artificial intelligence research in the 1960s. With the advent of the WEB2.0 era, the amount of information on the Internet has shown explosive growth. Traditional search engines can no longer meet people's needs for accurate information, and knowledge graphs have emerged as the times require. In 2012, Google launched the knowledge map and applied it to the search engine, which greatly improved the accuracy of the search.
With the continuous development of artificial intelligence technology, knowledge graph technology is also constantly improving and developing. At present, knowledge graph has become an important research direction in the field of artificial intelligence.
Technical principle
The realization of knowledge graph mainly depends on three technologies: semantic web technology, ontology technology and machine learning technology.
Semantic Web technology refers to the use of RDF and other standards to establish structured semantic descriptions and establish relationships between entities. Ontology technology refers to the definition of entity concepts, attributes, relationships and other information, and the establishment of mapping between ontologies. Machine learning technology refers to the use of machine learning algorithms to automatically extract information such as entities, attributes, and relationships, and to continuously optimize the accuracy and coverage of knowledge maps.
In terms of technical implementation, the knowledge map is a very complex technology that requires the cooperation and collaborative work of multiple technologies to realize it. Therefore, the construction of knowledge graph requires a huge team, including professionals in semantic analysis, machine learning, data mining and other fields.
Take the Baidu Knowledge Graph as an example. When a user searches for "Zhong Nanshan", the Baidu Knowledge Graph will automatically display Zhong Nanshan's basic information, resume, achievements and other related information. relationship between. In this way, users can not only understand the basic information of Zhong Nanshan, but also understand the relationship between Zhong Nanshan and other entities.
In addition to Baidu knowledge map, there are many knowledge map applications at home and abroad. For example, Google's knowledge map can help users find the information they need more accurately; Microsoft's Satori knowledge map can help the smart assistant Cortana provide better services to users.
Application Scenario
Knowledge graphs have been widely used in search engines, intelligent customer service, intelligent question and answer, intelligent recommendation and other fields. For example, knowledge graphs can help search engines understand users' search intentions more accurately, thereby providing more accurate search results; knowledge graphs can also help intelligent customer service interact better with users and improve user satisfaction.
In addition, knowledge graphs can also be applied to fields such as medical care, finance, and education. For example, in the medical field, knowledge graphs can help doctors more accurately diagnose diseases and formulate treatment plans; in the financial field, knowledge graphs can help banks better understand customer needs, thereby providing more personalized financial services.
A leading company in the knowledge graph industry
At present, the leading companies in the knowledge graph industry mainly include giant companies such as Google, Baidu, and Alibaba. These companies have huge data and technical reserves, and can continuously optimize the quality and effect of knowledge graphs.
At the same time, there are many emerging knowledge graph companies, such as Zoloz under Ant Financial, as well as domestic Zhihu and Xiaohongshu. These companies also have their own research and exploration in the field of knowledge graphs.
future development trend
With the continuous development of artificial intelligence, knowledge graph technology will also be continuously optimized and expanded. In the future, knowledge graphs will be more intelligent, humanized, and personalized, bringing more convenient and efficient services to people.
- Multilingualization: With the development of globalization, knowledge graphs in different languages will become more and more popular
- Cross-domain application: knowledge graph will be applied in more fields, such as agriculture, tourism, Internet of Things, etc.
- Intelligent application: the knowledge map will be more intelligent, able to better understand human needs and intentions, and provide users with more personalized services
In short, knowledge graph, as an important artificial intelligence technology, will play an increasingly important role in the future development, creating a more intelligent and efficient way of life for human beings.