What is semantic web?
Semantic web refers to a state where machines understand every piece of information available on the internet. This enables machines to process content at scale, and provide meaningful insights. They are also able to represent data in a structured manner, so it can be easily connected and reused. Other foreseeable benefits of the semantic web include complete automation with intelligent self sufficient systems, personalization on every front, and knowledge discovery, by linking new data and working on existing relationships to infer new relations, in quick time.
A common challenge that the semantic web faces is standardization of data. Without standardization, data would be available in various formats and languages. The common frameworks used to avoid this challenge include web ontology language (OWL) and resource description framework (RDF). These frameworks ensure the use of common data formats and exchange protocols on the web.
What is semantic analytics?
Semantic analytics helps us derive meaningful insights from available knowledge. Machines are able to understand text by interpreting sentences along with grammatical structures, to get a better understanding of the context that the text is referring to. The technology behind this - natural language processing (NLP) - is extensively used in building chatbots as well. Using semantic analytics, it is easy to access and draw meaningful insights from unstructured data, from various sources like emails, social media, or other legacy systems.
The word orange, for instance, has two meanings - one the colour and the other the fruit. Semantic analytics tackles this problem by identifying relationships between two entities and determining which meaning would fit better in the given context. A common semantic analytics model is sentiment analysis, where we try to decipher the emotion in a text. Based on the sentiment score, it is possible to define whether a text is delivering a positive, negative, or neutral sentiment. This model is very helpful in evaluating overall sentiments on any topic by analyzing tweets related to them.
Semantic analytics is commonly used to classify texts based on predefined categories. Take the case of support tickets - people often raise tickets in wrong categories and agents have to spend a lot of time assigning them to the correct department. This problem can be easily solved by using semantic analytics, as tickets can be sorted based on their content. Intent classification is also very well used to sort data points, based on a person’s interest.
What is a knowledge graph?
A knowledge graph can be referred to as a computer’s encyclopaedia. Information is stored in an organized way that a machine can understand and refer to. Using knowledge graphs, a relationship can be created between two entities based on their attributes. One of the most common use cases of knowledge graph is the Google search engine. It is powered by Google’s knowledge graph, which is often referred as “The Knowledge Graph”. The search engine provides the right search results even if we type two or three words in Google search. This happens because the knowledge graph analyzes what each word means in a search, rather than analyzing the entire string.
Knowledge graph stores information in a way that is similar to how we remember things and the relationships between them. For example, we might remember two common friends by considering a link between one friend and his/her friend. The only difference between a machine and humans is that we tend to forget and mix things up. But once a machine gets a relationship right, it stores it and never forgets it. We’ve often heard about metadata, that is, the description of data. The links between entities is also based on metadata and it lays a foundation for the knowledge graph. If we visualize a knowledge graph, it will look like a complex network where each entity is linked to the other based on some entity description.