The knowledge graph is a major step forward in the evolution of artificial intelligence (AI) programs. This software collects and processes semantic-search information to give you astute and original observations about any subjects you choose.
Every day, a knowledge graph’s engines crawl and extract data from millions of social media postings, blog entries, news stories and other online documents. Its taxonomy functions then allow it to merge similar data, make connections and draw insights that humans almost surely wouldn’t come up with themselves. In more technical terms, it turns unstructured data into structured data.
Ultimately, a knowledge graph can offer valuable advice on a wide range of topics, including for specific industries and companies. It also stores information about the relationships between data points. That way, a knowledge graph can determine people’s intentions when they enter keywords, and it can offer the most helpful responses possible.
Especially surprising is the fact that such a program often perceives people’s emotions. It can give you the ratio of positive to negative responses to a product, an event or something else entirely, and it can measure the intensity of these feelings as well.
AI and machine learning are vast fields that are divided into subgroups. One of those categories is knowledge representation and reasoning, the ability of a program to take complex pieces of information and sort through them to make decisions. As time passes, its algorithms will keep finding more effective ways of analyzing data sets. In other words, it can teach itself new things.
Here is an example of knowledge representation and reasoning: Imagine that a sick patient wants a diagnosis from a computer. The machine’s ontology capabilities would let it absorb a variety of data points, including the person’s past illnesses, symptoms and risky behaviors. It would weigh the relationships between all of those facts and, by employing classifiers and rules of logic, would then arrive at a conclusion.
In addition, knowledge graphs are excellent at using data to predict all types of outcomes, such as which hospital patients will suffer heart attacks and which high school students will drop out.
When knowledge graphs deploy natural language processing, people have entirely new ways of interacting with digital devices. They can have extended, detailed and profound conversations with their computers, tablets and smartphones.
Get ready: Thanks in large part to this software, the total body of human knowledge is set to expand exponentially in the coming decades.
