Knowledge Graph: Arranging & leveraging Information through interlinked domains, to find life solutions.

The advent of the internet and generation of loads of digital knowledge henceforth, flowing through the web have created a sea of information, about real-world entities, on the world wide web and it is increasing at a rapid speed, every moment. Have you ever wondered how this information is readily available whenever you need and how answers to your questions are independent of your geographic location? How this information is interlinked? This information is accumulated over time from billions of websites and pages created by users worldwide. Creation of Knowledge Graph (KG) is an approach to interlink worldwide information and leverage it through research, analysis and programmatic construct to find life solutions in multiple domains as Medicine, Technology, Artificial Intelligence (AI), Social, Forensic Science.

What is knowledge?

The concept of knowledge and its graph can be understood by considering the growth and development of a brain from birth onwards. The brain starts classifying, naming, and storing all information in designated areas of memory, for future use. Keep building vocabulary on this information, try to connect, compare, and create a pattern, and later leverage it to apply in life functions. This is the process of learning. This concept of taxonomy, ontology, and knowledge base creation is being used in the AI domain to find intelligent solutions.

What is Ontology?

Ontology is a philosophical approach in metaphysics that deals with the nature of existence or being. If a thing is said to exist it can be classified in different categories based on its differences and similarities to other entities, it is compared with. In this approach knowledge base created codifies some information and then an ontology is developed to reflect connections between different data elements. Ontology incorporates the concept of naming objects (taxonomy) and entities, assigning classes and attributes, collecting information, and preparing Knowledgebase. It also allows systems to communicate with each other through rich context and interlinked attributes in the knowledge base. Not only this it also communicates and works with multiple systems through workflows, processes and other program constructs to fully realise, ingest information and provide valuable insights.

What is the Knowledge Graph?

Knowledge Graph acquires and integrates information into a construct, an ontology, and apply reasoning to derive new knowledge and insight from it. KG is a set of datapoints linked together by relations amongst a domain, it can be people, a business, or an entity. Knowledge graphs can be built automatically through users and their information on the web.

Knowledge Graphs are considered as data architecture with nodes of preserved information, which are interconnected with respect to some relations or attributes. These data structures can be used or ingested by algorithms or neural nets for machine learning in AI, by performing tasks as classification, clustering, and regression. Knowledge in these graphs is categorized as general knowledge, site-specific and domain-specific knowledge, where general knowledge is site and domain-independent and can be acquired and distributed by various sources on the web.

Google Knowledge Graphs came into existence on May 16, 2012 and since then-incoming data have created accurate and structured knowledge panels and created connections and platforms of people, places, things, and entities.

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Next, we will see how to create Knowledge Graphs