Semantic Web can help with explainable AI. However, Semantic Web complements and even overlaps with AI/ ML. Though some ML algorithms require labelled data for training, there's no need to add semantic metadata to all data. This has been possible because of the availability of data to train ML algorithms. The mid-2010s have seen the arrival of voice assistants, chatbots, computer vision applications, and more. It therefore does appear that AI/ ML has gone ahead and enabled machines to see, hear and speak. Instead of asking machines to understand humans, we help machines to solve well-defined problems on well-defined data via well-defined operations. Semantic Web doesn't make machines intelligent in the sense of Artificial Intelligence or Machine Learning. With the progress of Machine Learning ( ML), is Semantic Web relevant?ĭifference between Semantic Web and Data Science.For example, this could be useful in discovering new drugs. Semantic Web enables machines to apply logic on existing relationships and infer new ones. Knowledge Discovery: By linking data across the web, new knowledge can be discovered.Data that was previously stored in isolated databases can now be shared in a standard manner. Data Reuse: Because Semantic Web enables linking of data from a variety of sources, data can be reused.Information Retrieval: Within enterprises or via web search engines, Semantic Web can give us more relevant answers.Agents can personalize or curate content for us. It's impossible for us to follow everything. Personalization: Content on the web is growing daily.These can be efficiently handled by virtual assistants or agents. Automation: We can avoid doing mundane stuff such as booking tickets or rescheduling appointments.What's the motivation behind Semantic Web?īy enabling machines to understand data, we can benefit in many ways:.Semantic Web wants to transform the web from a 'web of documents' into a 'web of data'. Semantic Web is an attempt to describe and link web content in a manner that's meaningful to machines. It didn't know that the heading is actually the title of a blog post or that the text in italics is the author's name. For example, a computer could tell that a particular text is a heading or another text is in italics. Servers and desktop computers processed or displayed all this information but didn't understand them. Via hyperlinks, these linked to other webpages or documents anywhere on the web. In the original web of the 1990s information was shared as webpages or documents that could be understood by humans. Semantic Web, from web of documents to web of data.

How to create semantic rules examples