Inference Near the top of the Semantic Web stack one finds inference — reasoning over data through rules. Vertical Applications W3C is working with different industries — for example in Health Care and Life Sciences, eGovernment, and Energy — to improve collaboration, research and development, and innovation adoption through Semantic Web technology. News None. See news archive. Events None. Others, such as the storage of high volumes of highly structured transactional data, do not.
Understanding when it is a good idea and when it is not a good idea to apply Semantic Web technologies is one of the primary objectives of the Semantic University. These topics are addressed in more detail in future lessons. From a technical point of view, the Semantic Web consists primarily of three technical standards:. One way to differentiate a Semantic Web application vs. However, the Semantic Web has been called many things, such as Web 3. The knowledge graph construct has emerged to help developers and decision makers to more tightly constrain the development and application of the Semantic Web standards.
Tools and techniques have matured such that enterprise scope and scale knowledge graph applications are feasible and ready for mainstream use. Similar to the growth of Web 1. Semantic Web technologies as a whole have made tremendous strides in the last decade. Some highlights include:. Truly, an entire industry has been born in the past ten years, complete with multiple trade shows on several continents, a growing user community, and active standards bodies.
That said, significant room for growth still can be found. Discover how the Core Semantic Model empowers organizations with intelligent content experiences. What Is the Semantic Web? The Semantic Web is the knowledge graph formed by combining connected, Linked Data with intelligent content to facilitate machine understanding and processing of content, metadata, and other information objects at scale.
Semantic standards unlock a crucial evolution of the web towards intelligence that allows the content we post online to be presented in a way that can be understood, connected, and remixed by machines.
AI will always remain niche applications built against a limited corpus of content until structure and semantic standards exist across content sets. Adopting Semantic Web approaches to content gets publishers closer to globally-machinable sets of content. Engineering the Semantic Web Content engineers are creating a more powerful and agile web of content and data by first parsing and structuring the discrete elements of content that constitute websites, such as people, events, ideas, concepts, products.
When such machine-readable descriptions are present, they can be linked to build a more robust web of data where computers can find, read, and even reason about a unit of content.
We can see the application of semantic data in various places throughout the web, such as those in certain search experiences. Because of this rich, new layer of information, search engines and other bots are able to provide the most relevant content directly to the user, edited to the most important snippets that save humans time and effort.
The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Linked Open Data LOD is structured data modeled as a graph and published in a way that allows interlinking across servers. This was formalized by Tim Berners-Lee in as the Four rules of linked data :. LOD enables both people and machines to access data across different servers and interpret its semantics more easily. As a result, the Semantic Web transcends from a space comprising of linked documents to a space comprising of linked information.
Which, in turn, empowers the creation of a richly interconnected network of machine-processable meaning. Today, there are thousands of datasets published as LOD across different sectors such as encyclopedia, geographic data, government data, scientific database and articles, entertainment, traveling, etc.
Because of their linking, these datasets form a giant web of data or a knowledge graph , which connects a vast amount of descriptions of entities and concepts of general importance. For example, there are several descriptions of the city of Varna e. Semantic metadata amounts to semantic tags that are added to regular Web pages in order to better describe their meaning.
For instance, the home page of the Bulgarian Institute for Oceanography can be semantically annotated with references to several appropriate concepts and entities, e. Such metadata makes it much easier to find Web pages based on semantic criteria.
It resolves any potential ambiguity and ensures that when we search for Paris the capital of France , we will not get pages about Paris Hilton.
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