Do you ever wonder how Netflix recommends movies of your choice?  Or how do airports streamline their flights? Or how cell phone towers are connected? Or any other application that deals with understanding the human context of data connections. Well, the technology at play here behind all these applications is a Knowledge Graph.

A knowledge graph is a knowledge management tool that combines and organizes information as an interconnected data network. It applies semantics to give a deeper, dynamic context to data. Knowledge graphs are designed to contextualize complex data for building human-centered systems. 

Owing to the increased demand for context-driven insights, knowledge graphs have become crucial components of numerous everyday applications. From building data networks and breaking data silos to advancing AI and digital twinning, knowledge graphs bring the whole package to the table. Hence, it is no surprise that knowledge graphs have made their way into many industries that are on their journey to becoming data-driven. In fact, Gartner predicts that knowledge graphs will be a part of 80% of data and analytics innovations by 2025.

The numerous knowledge graph benefits are encouraging industries to leverage graph technologies to understand their data. In this article, we will list some of these knowledge graph applications spanning various industries. 

Healthcare

The enormous amount of medical data is both a blessing and a curse for the healthcare industry. Fortunately for AI advancements, knowledge graphs are growing popular in the medical field for tackling this curse and enhancing the benefits of large medical data sets. The healthcare industry is now increasingly adopting knowledge graphs to understand the semantic interconnection between their medical datasets. In addition to saving time and money, the most crucial application of knowledge graphs is to assist healthcare professionals in saving precious human lives.

Knowledge graphs find their applications in the following areas of the healthcare domain:

• Integrating medical knowledge: Healthcare data is the most diverse dataset of all. It is an endless pile of rich information from biomedical, genomic, and protein to molecular data. Knowledge graphs can effectively gather and structure medical data for different healthcare domains to assist in medical research.
• Mapping relationships between medical data: By mapping the correlations between heterogeneous medical data, knowledge graphs enable researchers to easily navigate vast medical datasets to uncover specific diagnoses and treatment options for each patient.
• Drug discovery: The potential to cure life-threatening diseases, drug discovery is the most important application of knowledge graphs in healthcare. With historical data at hand, researchers can infer new ways in which a drug can be used. Wisecube’s knowledge graph is an example of graph technology usage in drug discovery. 
• Patient Care: One other important application of knowledge graphs in healthcare involves monitoring patient information and predicting risks and anomalies in their data. Graphs are excellent for for multi-variate anomaly prediction as outlined here.

Internet of Things (IoT)

IoT is a growing industry that continues to bring us digitized appliances and devices for our convenience in everyday life. It is a large industry with billions of connected devices producing large amounts of data. Integrating and decluttering this data for meaningful actions is a challenging task. 

Knowledge graphs address these challenges with their ability to handle large volumes of data from heterogeneous sources. They can benefit the IoT industry by enriching their user experiences and creating better economic opportunities. Knowledge graphs apply to the IoT domain in many ways, including:

• Centralized view of IoT information: Knowledge graphs provide a holistic view of diverse data coming from different devices. This integrated view makes data exploration and querying easier than ever before. Moreover, knowledge graphs offer edge control and smooth interoperability and collaboration between devices.
• Virtual Assistants & Chatbots: Knowledge graphs for smart assistants and chatbots are live applications of graph technology in the IoT industry. They can be queried for information regarding almost any domain because of their underlying knowledge graphs.

Finance & Banking

The banking and finance sector is responsible for managing critical customer and transactional data. It is vital for them to track their customer’s behavior and the flow of money to avoid unauthorized transactions. 

Knowledge graphs have emerged as a blessing for the finance industry by offering them a secure way of managing their financial knowledge base. Goldman Sachs is a popular example of a banking institution that uses knowledge graphs for transaction and customer analysis. 

In addition to analysis, knowledge graphs find many applications in the financial domain, including:

• Transaction surveillance: With a semantic network of financial information, banking institutions can explore and track the different ways money flows between their users. This gives them a holistic view of their clientele and a better understanding of their users’ behavior.
• Financial crime detection & prevention: By tracking banking information with a centralized view, banks can detect or even predict unauthorized transactions. Knowledge graphs provide banks with sturdy protection against all kinds of financial crimes, including fraud, corruption, and money laundering.
• Non-compliant user detection: Studying user behavior is an important aspect of understanding the customers. In addition to customer personalization, knowledge graphs also allow banks to detect customers not complying with their policies so that they can take timely action against them.

Media & Entertainment

The entertainment industry, specifically social media, is one of the industries that are significantly benefiting from knowledge graphs. With AI already playing its part in the entertainment, knowledge graphs are serving as a cherry on top to bring user satisfaction to a whole new level.

Following are some of the many applications of knowledge graphs in the media industry:

• Social Graphs: Social media platforms use knowledge graphs to build social graphs of their users to help them maintain and view their users in the context of their connections with other users. Facebook’s Entity Graph is a social graph created by Facebook.
• Recommender Systems: Predictive knowledge graphs enable media platforms to recommend the most relevant items to their users based on a map of the user’s specific interests. For example, movie recommendations by Netflix and friend suggestions from Facebook use predictive knowledge graphs.
• Content Targeting: Using the structural relations between a user and its likes and dislikes through a behavioral knowledge graph, media platforms infer user preferences to deliver targeted content to user’s feeds.

Search Engine

The internet is an ocean of information that is continuously growing with time. Behind every search query, a search engine navigates through this ocean to retrieve accurate search results. Knowledge graphs have been an important part of this navigation ever since Google announced the first knowledge graph. 

Two most popular search engine knowledge graphs today include the Google knowledge graphs and the Bing knowledge graph by Microsoft. These two knowledge graphs have boosted the way modern search engines operate in the following ways:

• Search enrichment: By developing rich connections between the information stored on the web, knowledge graphs have refined search down to very specific contextual details. Search engines use knowledge graphs to create the best summary that relates to the search query.
• Quick information retrieval: Thanks to knowledge graphs, traversing through large volumes of data is faster than ever before. The semantic web makes it easier to go deeper and broader to find the relevant path to navigate for every search query.
• Voice searching: With knowledge graphs, search engines can perceive entities and properties from natural language voice searches.

Cybersecurity

With the growing digitization of industries, there is a significant increase in the amount of personal information being stored and exchanged online. Consequently, the risk of storing and moving growing volumes of sensitive data is now at an all-time high. Industries are now resorting to knowledge graphs for secure knowledge management of their critical data.

In addition to enhancing cybersecurity practices of other industries, the knowledge graph technology also finds its applications in the cybersecurity domain itself is also advancing. Knowledge graphs can be implemented to improve cybersecurity strategies in the following ways:

• Mapping historical cyber attacks: Historical cyber attack data is filled with insights that allow security professionals to detect patterns that can help strengthen security practices.
• Attack or breach prediction: A cybersecurity knowledge graph also finds its usage in predicting a future attack. By mapping the endpoints between systems, the knowledge graph gives a clear view of vulnerable areas that are exposed to the risk of attack.
• Filling security gaps:  A network of cybersecurity data enables organizations to holistically view their security structure to find any gaps in their security implementations. By filling in these gaps, organizations can block malicious attacks from sneaking into their systems.

E-commerce & Retail

The e-commerce industry generates and handles bulk loads of customer and product data. The correlations between the disparate online customer and product information are crucial for retail stores to design the best marketing strategies. This is where knowledge graphs entered and changed the way modern e-commerce brands work with their data. The most popular among these brands is the example of eBay’s knowledge graph.

By creating product and customer knowledge graphs, e-commerce businesses can apply this knowledge management technology in various ways, some of which include the following:

• Product Recommendations: By creating product knowledge graphs, online stores can map the relationships between customers and products to determine product recommendations based on customer preferences.
• Product Demand Analysis: Brands analyze their product knowledge graphs to determine and even predict the demand for a specific product.
• Customer Behavior Analysis: Knowledge graphs have opened new gateways for personalization. By digging deep into their customer behaviors, online businesses can now design targeted marketing strategies for a higher customer satisfaction rate.

How Wisecube’s Biomedical Knowledge Graph Assists Medical Research

Due to the rapidly increasing world population, biomedical is growing faster than analysts can keep up with. In order to address this issue, Wisecube developed an open-source knowledge graph engine for tackling the complexities associated with biomedical data. 

The Wisecube Knowledge Graph Engine is a platform for unifying and synthesizing your private and public biomedical data for disparate data sources. These sources include biomedical literature, chemical, protein, and side effects databases. You can also customize the graph to add proprietary data sources.

By centralizing biomedical knowledge, Wisecube helps organizations build a connected graph of concepts and evidence from millions of documents and databases, uncovering explicit connections and inferring undiscovered links using cutting-edge NLP.  It focuses on the things you care about and synthesizes them into easy-to-understand topics and insights so you can keep track of complex research fields.

If you are looking to explore patterns, uncover insights and make discoveries in your biomedical research area, schedule a call with us today and get started with knowledge graphs.