Expert ai S p A: What is a Knowledge Graph?
The human brain acquires knowledge by establishing connections between neurons. The more links he builds, the more knowledge he can acquire. This is true for the human mind and for artificial intelligence (AI). For AI to learn and understand, it must make connections between data. This is where knowledge graph technology comes in.
A knowledge graph helps define relationships between data. It can help bridge data silos and create data structures to accelerate machine learning (ML) and AI, making them fundamental for digital initiatives and data science.
According to Gartner, up to 50% of AI-themed inquiries involve discussions about graph technology, and it’s easy to see why. Knowledge graphs provide the fuel AI and ML need to learn from data and uncover patterns that reveal untapped opportunities.
Google Assistant, Siri and Alexa are all driven by knowledge graphs. Otherwise, how would they know that your question about Apple is related to the company that sells computers and phones rather than a fruit you can eat?
Here’s what you need to know:
Knowledge graphs focus on relationships
A knowledge graph connects data points to show relationships and describe a domain. It reveals relationships between features using nodes, edges, and labels. A node represents an entity (eg, bird), an edge shows the relationship between entities (eg, bird to nest), and a label captures the meaning of the relationship (commensalism). Together they define “bird” and “nest” by showing how they relate to each other.
Along with ontologies and taxonomies, knowledge graphs help machines interpret data more accurately. While ontology and taxonomy provide hierarchical and structural organization, knowledge graphs show correlations that further define them. Consider the word “lavender”. A knowledge graph would show the relationship between lavender and entities such as plant, color, scent, and the connection to objects such as shoes, scented candles, and wildflowers.
Knowledge graphs turn data into knowledge
A knowledge graph is a database of real facts that ML algorithms can use to improve their performance. They can be linked to establish links between entities such as “bird feeder”, “bird bath”, “bird cage”, etc. Because it defines entities and their relationships, reasoning and knowledge can be inferred by machines faster with more accuracy.
A knowledge graph solves problems of ambiguity, vagueness, and incompleteness of data while adding semantic structure to unstructured data. For this reason, they are at the heart of many AI systems, such as Siri and Watson. They are also used for natural language processing (NLP) tasks such as question answering, information retrieval, and machine translation.
Not all knowledge graphs are the same
Different types of knowledge graphs are used for different business initiatives. Some are used to automate processes such as governance, compliance or risk management. Others facilitate decision-making by revealing trends and predictions from data. Knowledge graphs can ultimately be used to impact any language-intensive manual process.
Consider the following concrete examples:
Netflix uses a predictive knowledge graph as the basis for its recommendation engine.
Google’s Knowledge Graph provides information about businesses, people, places, movies, books, and other entities on demand from Google Search or Google Assistant.
Facebook’s Entities Graph maps users’ social connections and those meaningful data connections to gain insights into preferences so it can deliver targeted content to users’ News Feeds.
The use of knowledge graphs is on the rise
A knowledge graph can function as a base layer to store facts on which ML processes can be run. Search engines can use them to understand user queries and return more relevant results. Companies can also use them internally to power applications that answer employee questions quickly and accurately.
There are valuable use cases for knowledge graphs across all industry sectors. They can:
identify financial and banking data patterns indicative of fraud.
connect structured and unstructured sources to break down silos and accelerate drug discovery for pharmaceutical organizations.
draw knowledge from health care resources so that the disease can be detected earlier in patients.
How Knowledge Graphs Make AI Smarter
Although ML is essential for the AI space, several challenges need to be overcome. More specifically, the availability of data. Unless you are Amazon or Google, it is not easy to collect large amounts of data to train ML algorithms. After data availability, there is accuracy. Machine-to-machine learning can be flawed if machines misinterpret the data.
A knowledge graph provides deep semantic context to data so that machines can be trained more accurately. It can produce rich datasets and real facts that ML algorithms use to improve performance. They are crucial for ML-powered systems and services because they provide human-like understanding of data.
Knowledge Graphs by Expert.ai
Accelerating your AI initiatives is easy with an accurate and robust knowledge graph at your fingertips. We facilitate access to our knowledge graph via the expert.ai platform. Out of the box, out of the box, it’s packed with domain expertise and easily customizable to your specific use case.
With this built-in knowledge, you can significantly reduce the time, cost, and labor associated with creating your own knowledge graphs. Plus, by leveraging a hybrid approach that combines machine learning and symbolic AI techniques, you can easily leverage this knowledge and improve the language models you create.
Knowledge is power. Turn that knowledge into AI success.