Most companies deal with a lot of data, but many aren't making using that data to drive smarter decisions or explore new opportunities. Knowledge Graphs can help companies extract insights from data they generate. Knowledge Graphs have been around for quite some time, but gained popularity after Google implemented them in search engines. Gartner identified Knowledge Graphs as one of the emerging technologies in its Hype Cycle for Artificial Intelligence. Knowledge Graphs can be used by businesses of all sizes to make data easily accessible and searchable.
In simple terms, a Knowledge Graph represents a collection of interlinked entities. The use of semantic metadata (descriptions) adds context which can then be processed by humans or AI algorithms.
To understand how Knowledge Graphs are being used by businesses, it's best to start with Google’s implementation of its Knowledge Graph. Google uses its Knowledge Graph to answer very specific questions and displays answers on its search result page. Since Google doesn't' have content of its own, the information is retrieved from structured, interlinked, and authoritative sources across the web. The Knowledge Graph is also used to answer voice queries made through Google Assistant and Google Home.
LinkedIn on the other hand is a social network for professionals, its Knowledge Graph consists of entities such as "members" and has interlinked information about them such as “skills”, “jobs”, “companies”, “geographical location” etc. The relationships between these entities (such as “member” and “skill”) is what LinkedIn's Knowledge Graph is built on. Together, the “entities” and “relationships” between them form the basis of the LinkedIn ecosystem. This helps LinkedIn enhance its recommendations, search results, monetization, consumer products, and business analytics.
However, it is not just the big tech companies that can implement Knowledge Graphs, any company with a large amount of data stands to benefit from Knowledge Graphs.
A lot of data within an organization is unstructured, which makes it difficult for businesses to utilize it meaningfully. Even when data is structured, it's stored in data silos presenting only a partial picture which can lead to poor decisions. By creating a domain-specific web of information, information can be found much faster and better decisions can be made with a full picture view of all relevant data.
In many organizations, knowledge sharing is important for employee development. A Knowledge Graph of semantically connected internal assets helps employees discover relevant information. Based on factors such as skill-level, interest, location, etc. a knowledge graph makes content easy to find.
Knowledge Graphs can play a significant role in implementing AI strategy. It's well known that Google's search engine is driven by AI, but it's important to note that Google had already implemented it's first Knowledge Graph in 2012. While tech giants may utilize Knowledge Graphs for services like Google Assistant, Siri, and Alexa, businesses can benefit by using AI-powered Knowledge Graphs at a much smaller scale, for instance to power customer service chatbots, RPA bots or internal virtual assistants.