Knowledge Graph

Have you heard the term “knowledge graph” and its different uses❓ If you are not familiar, 🤔this article covers the basics for a beginner. 

The concept “knowledge graph” has been used in literature very early.  In 2012, Google announced a product called the Google Knowledge Graph, and a number of other large companies 🕵️ soon followed with their own knowledge graphs. 🎓Academia began to adopt the knowledge graph term to loosely refer to systems that integrate data with some structure of graphs, a reincarnation of the Semantic Web, and linked data. Today, the concept have begin to have more applications on 💱many business cases.

Why is knowledge graph needed?

Data is all around us, 📝 every time we use a device, every time we make a transaction, every time we make a phone call, there is a piece of data created! Essentially, the world is a Big Data suite and these connections keep grow too. Understanding the connections between different data 🔍is critical for better data security and data protection. We need to identity a bad trigger or identify possibility of a missed bad incident. To analyze this, we need to understand connected data. The best way to understand connected data is to visualize it! Human brain is incredibly good at processing images. Hence the need for graphical representation of connected data.

What is a knowledge graph?

A knowledge graph, also known as a semantic network, represents a network of real-world entities—i.e. objects, events, situations, or concepts—and illustrates the relationship between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term knowledge “graph.”

As of now, the study of knowledge graphs stands at the junction of many areas of computer science:

✅ information retrieval, which allows you to speed up the filling of the graph from various sources;

✅ #natural language processing (NLP) and semantic technologies that allow for describing and using the meaning of the stored knowledge in the analysis;

✅ #datamanagement systems, providing efficient storage of graphs; and

✅ #machinelearning machine learning, allowing for analyzing the knowledge contained in the source data and generating new knowledge.

Components of Knowledge Graph

In its simplest form, a knowledge graph is a directed labeled graph that comprises three elements:

📌 nodes — real-world entities that can be both material things and abstract concepts;

📌edges — links that connect the nodes; and

📌labels — attributes that define the relationships between the nodes and reasoning rules on edges.

How does a Knowledge Graph work?

Knowledge graphs are typically made up of datasets from various sources📚, which frequently differ in structure. Schemas, identities and context work together to provide structure to diverse data. Schemas provide the framework for the knowledge graph, identities classify the underlying nodes appropriately, and the context determines the setting in which that knowledge exists. These components help distinguish words with multiple meanings. For example , it determines the difference between Apple, the brand, and apple, the fruit.

➡️1.      Knowledge graphs, that are fueled by machine learning, utilize natural language processing (NLP) to construct a comprehensive view of nodes, edges, and labels through a process called semantic enrichment.

➡️2.      When data is ingested, this process allows knowledge graphs to identify individual objects and understand the relationships between different objects.

➡️3.      This working knowledge is then compared and integrated with other datasets, which are relevant and similar in nature.

➡️4.      Once a knowledge graph is complete, it allows question answering and search systems to retrieve and reuse comprehensive answers to given queries.

➡️5.      While consumer facing products demonstrate its ability to save time, the same systems can also be applied in a business setting, eliminating manual data collection and integration work to support business decision-making.

➡️6.      The data integration efforts around knowledge graphs can also support the creation of new knowledge, establishing connections between data points that may not have been realized before.

Enterprise Knowledge Graph

Organizations increasingly rely on knowledge graph tools to make the most of their growing volumes of data. Information from across the enterprise is integrated into a single, large network. The network contains a semantic model of the data for users to query and explore. In this way, 🏆raw data transforms into knowledge👑. Effective knowledge graphs are accessible to everyone – not just developers and expert analysts. That’s where visualization is critical.

Significance and impact of knowledge graphs

Knowledge graphs have emerged as a 💪powerful and versatile tool In AI and data science for representing structured information, enabling efficient data retrieval, reasoning, and inference❗️It has become essential for organizations with vast amounts data and are wanting to make sense of interconnected information.

🔸 Knowledge graphs offer a structured representation of information in a graph format with nodes, edges, and properties.

🔸 They enable flexible data modeling without fixed schemas, facilitating data integration from diverse sources.

🔸 Knowledge graph reasoning allows for inferring new facts and insights based on existing knowledge.

🔸 Applications span across domains, including natural language processing, recommendation systems, and semantic search engines.

🔸 Knowledge graph embeddings represent entities and relationships in continuous vectors, enabling machine learning on graphs.

As research and technology advance, knowledge graphs will 💯undoubtedly play a central role in shaping the future of AI, data science, information retrieval, and decision-making systems across various sectors.