We humans are very good at collecting and processing information. Just imagine how we think when asked, “What do you think of when you see this picture of a dog?” A human would not need to run an elaborate algorithm like the closest neighbor classifier. We’d immediately associate it with mammals (rather than humans or inanimate objects) and categorize it as a dog.
However, it is not the case with data technologies and applications. When it comes to connecting data that is stored as tables, it takes a lot of iterations. Filtering processes, for example, are inefficient when they manifest relationships as table JOINS, which clog data pipelines. Because of the many tables, indexes, and lookup requirements, data science approaches like collaborative filtering necessitate a large number of JOINS.
These approaches tend to slow iterations down because they are either computationally intensive or require human involvement. Using graph technology, we quickly extract predictive features and reshape the data to be usable in an AI and machine learning pipeline. A simple graph query accelerates the process by returning a subgraph containing only the needed data.
What is graph technology?
Graph technology is touted to be one of the top analytics and data trends today because of its significant potential for disruption. It has become a groundbreaking way for organizations everywhere to capture and explore the relationships and connections between data entities used in data analysis.
This technology provides graph models to represent relationships. It allows users to apply pattern recognition, classification, statistical analysis, and machine learning to these models, enabling more efficient analysis at scale against massive amounts of data.
When it comes to graph analysis, algorithms look at the paths and distances between the vertices and the relevance of the vertices and their clustering. Incoming edges, the relevance of surrounding vertices, and other indicators are frequently used by algorithms to determine importance.
Because graph databases explicitly store relationships, queries, and algorithms that rely on vertice connectivity can be executed in milliseconds rather than hours or days. Users won’t have to perform as many joins, and the data will be easier to analyze and use for machine learning to learn more about the world around us.
Two types of graphs
Property graphs and RDF graphs are the two types of graphs. The property graph is more concerned with analytics and querying, whereas the RDF graph is more concerned with data integration. A set of points (vertices) and the connections that connect them make up both types of graphs (edges). There are, however, some distinctions.
Property graphs can model relationships between data, enabling query and data analytics based on these relationships. A property graph has vertices containing detailed information about a subject and edges that denote the relationship between the vertices.
RDF graphs, which stands for Resource Description Framework, represent statements and best represent complex metadata and master data. They are used to represent complex concepts in a domain or situations requiring rich semantics and data inferences.
A statement is represented by three elements in the RDF model: two vertices connected by an edge. A unique URI, or Unique Resource Identifier, is assigned to each vertex and edge. The RDF model enables information exchange by publishing data in a standard format with well-defined semantics. The RDF graph has been adopted by government statistics agencies, pharmaceutical companies, and healthcare companies.
Graph technology connects data, defines relationships, and effectively empowers the development of sophisticated AI applications. Due to its benefits, organizations everywhere are turning to graph technology. Some of the most popular uses of graphs across industries include preventing money laundering, detecting money mules and mule fraud, real-time fraud detection, increasing traceability (contact tracing), master data management, tax fraud detection, cyber security, product recommendations, etc.
Graphs provide context for AI in at least four different ways. The first is knowledge graphs, which provide context for decision support and ensure that responses are appropriate for the situation. Second, because graphs have a higher processing efficiency, graph accelerated machine learning uses graphs to improve models and speed up processes. Third, connected feature extraction examines data to determine which elements are the most predictive. Finally, graphs can be used to provide insight into how AI makes decisions. This is referred to as AI explainability.
Since data is already connected in the graph model, allowing relationships with multiple degrees of separation to be traversed and analyzed quickly at scale, graphs provide context for improved efficiency for machine learning algorithms. As a result, graph-accelerated machine learning was coined.
In short, graphs unlock the potential of AI and machine learning. That’s because graph technology incorporates context and connections that make AI more broadly applicable.