July 20, 2023 7:44 AM
In the early 2000s, all major companies started focusing on the latest and greatest technologies and became tech-centric. The same companies further evolved themselves to being data-centric in the 2010s. Nowadays, in order to keep up with the evolving times, these companies have further progressed and are focused on being a decision-making company by gaining understanding of rich data relationships.
A Knowledge Graph (KG) is a natural data model in many real-world situations. KGs captures useful relationships and, with the help of graph embeddings, it aggregates a vast amount of knowledge(domain) into the lower dimensional representation (embedding spaces).
It’s not just the knowledge, but the interpretation of the knowledge. The ability to think and see how these pieces of information connect plays a huge role in extracting value from an organization's data.
A graph is a natural way to express a collection of objects and their connections. In a real-world scenario, relationships between an object with any other related object can be very easily defined using a graph. These relationships can be articulated by using graph topologies such as nodes, edges, and attributes, allowing for semantic searches or exploring similarities.
A common implementation of graphs is within social networks, for example, graphs can be used to view people who are linked/associated to each other in the network, such as finding a friend of friend, suggestions of new friends, finding events based on you and your friends’ common interests, or even capturing important dates such as the date you became friends or other shared life events.
Graphs are the most practical way to collect and connect the high-dimensional data points and depict and explore the relationships between all of the data points that are available from various sources and in different forms.
2. Knowledge Graph Basics
A graph is a representation of schema-free objects (vertices or nodes) along with relationships between the objects (edges). It is a network of data points formed by vertices and edges. Formulating a domain-specific graph results in a “Knowledge Graph (KG)”, which represents all the information about nodes and relations between nodes in the given context.
A Knowledge Graph (KG) consists of the following core elements:
Graph Neural Network →
Graph Embeddings →
3. Knowledge Graph System
KG incorporates the domain knowledge by consuming the SQL-like data structure from the enterprise data (primary and foreign key relationships) and transforming it into a Graph-like representation.
We further learn the relationship between multiple entities by training the graph data on state-of-the-art Graph Neural Network (GNN) algorithms in a supervised way. During the learning process, it encapsulates relevant relations and context around each entity by summarizing them into Graph Embeddings in vector form.
Each node and edge can be visualized in a 2D plot. Users can interact with this 2D plot to examine, explore, annotate and take relevant actions such as creating clusters (knowledge spaces) and transforming them into features or rules.
A segment of enterprise data that captures common behavior among entities can be grouped together. Users can use the “Graph Annotation” flow to create such Knowledge Spaces. It will also recommend some high-quality spaces which can be relevant for the users such as spaces for good/bad users, etc.
Each of these spaces has something unique to narrate about the enterprise data. Users can group various data behaviors, such as “high-paying male customers” into one space for exploration;
Users can also group model behaviors, such as “all the False Positive predictions” into features or rules for data and model enrichment.
4. Knowledge Graph and Beyond
Knowledge graph with explainable AI capabilities, offering a wider spectrum of semantic search, new cohort exploration, impactful feature adoption, a model improvement over time with a human in the loop for feedback integrations, and more.
5. Identifying use cases
5.1 Use cases for knowledge graph systems
Rahul Kumar is an AI scientist, deep learning practitioner, and independent researcher.See other posts from this Author