Knowledge graphs (KGs) have been around for quite a while, but they didn’t receive much attention until Google began integrating them into its search engines. Today, large companies like Google, LinkedIn, and Amazon use KGs to optimize searches, but companies of any size can use them to improve data accessibility and searchability.
Today’s emphasis on searchability is forcing content marketing and search engine optimization (SEO) experts to create rich networks of informative and instructional materials to satisfy customers during the buyer journey. Companies that don’t excel at searching and retrieving data for their customers have trouble remaining competitive. Using a methodological system like KGs to more efficiently manage that data thus becomes a strategic advantage. For example, if a person wants to search Google for his or her favorite place to eat but only knows the location and not the name of the restaurant, Google, with the help of its KG, can provide relevant suggestions in real time. Similarly, KGs can improve a company’s content marketing and SEO by: (1) unambiguously defining content for search engines and (2) building robust information environments around products and services for prospects and customers.
Business Uses
One of the most important KG functions is creating linkages across multiple data sets. By providing a visual representation of the underlying connections between data nodes, KGs help leaders advance their understanding of their environment so they can make intelligent business choices. Here are four examples:
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By providing a way to merge data silos, KGs create a valuable overview of all knowledge in a company, both within departments/divisions and across them. This is helpful for companies with multiple divisions, especially if they’re located in different regions or countries.
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KGs have the ability to connect different kinds of data in meaningful ways. For example, academic graphs include people, papers, research topics, and conferences to help users detect connections between researchers and pieces of research.
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By narrowing searches and contextualizing information, KGs can help business leaders make more informed decisions faster.
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By having each topic or item represented just once (with all its connections) in context with all other subjects and their relationships, KGs clearly show how each node is interconnected. This helps leaders gain perspective on how important ideas relate to one another.
Real-World Examples
The benefits of KGs are not limited to large tech companies. In fact, any company with a significant amount of data can benefit from them. Following are some examples of how companies are using KGs to improve content management and user-centric services — and how other companies could follow suit.
The search results page on Google responds to questions the company has already addressed with the help of its KG. Since Google does not develop content, the results it displays originate from credible sources that are organized and linked, yet dispersed over the Internet. Voice-activated assistants Google Assistant and Google Home use the same KG to answer verbal inquiries. In other words, Google’s KG is a knowledge base designed to improve its search engine results using information acquired from a variety of sources. Following its launch in 2012, Google’s KG saw tremendous growth, more than tripling in a matter of months to reach 570 million entities and 18 billion facts by its most recent count.
Rather than crawling through or indexing websites, Google uses its KG to organize the world’s information by topic; advantages for the company include scale, data integrity, and speed. Google can easily harness user behavior data to understand what topics are significant to individuals and suggest topics based on user history. Other companies could use this approach, leveraging data to better understand customer behavior in order to improve products and/or marketing.
AWS
Amazon Web Services (AWS) KGs are a mechanism for modeling and conveying knowledge about the company’s services. This concept has been around for a while, but the development of scalable graph databases has made it more applicable. Compared to data management systems like relational databases, KGs are extraordinarily adaptable, capable of accounting for the variety and heterogeneity of data in the real world.
Using a collection of ideas, the properties of those concepts, the interactions between those concepts, and the logical constraints that are expected to hold, AWS KGs can capture the semantics of a specific domain. Because this model includes logic, we can reason about graphs and the information included within them, making the information implicit in the graph readily available. The process of information asset consolidation includes integrating an organization’s information assets and making them easily accessible to all members of an organization.
AWS KGs open the door to a variety of applications, most of which are helpful on their own, not only for the company but for its clients. For example, Amazon could turn the data it gathers into a more helpful resource by using an enterprise KG. Furthermore, it could develop corporate knowledge graphs by using the built-in federated query functionalities of the Amazon Neptune graph database. Public data from the Internet could be used to enrich the information already included within these graphs. Other companies can similarly use KGs to help them organize information from dissimilar data sources to enable more intelligent search. Ultimately, KGs can help organizations make their data more understandable by using business terms rather than ambiguous codes.
LinkedIn’s KG is an enormous knowledge base constructed from entities such as members, jobs, titles, skills, companies, geographical locations, schools, and the connections between them. LinkedIn uses this ontology to improve its recommendation system; search, monetization, and consumer product offerings; and business and consumer analytics.
Developing this type of comprehensive knowledge base proved extremely challenging. Websites like Wikipedia and Freebase are almost entirely dependent on user contributions. LinkedIn took a different approach. LinkedIn’s KG is primarily derived from the large quantity of content provided by corporate administrators, recruiters, advertisers, and other users.
The KG grows constantly as individuals sign up for the platform, employment opportunities become available, new companies join, new skills are added, and new titles surface in user profiles and job ads. Moreover, the company uses machine learning (ML) methods to help find solutions to its KG network challenges. This is essentially a process of data standardization on user-generated content and external data sources. ML is applied to entity taxonomy construction, entity-relationship inference, data representation for downstream consumers, insight extraction from the graph, and interactive data acquisition from users to validate inferences.
New entities are continuously added to the KG, and new connections are forged between existing entities. Alterations to existing partnerships are also possible. For instance, when a member gets a new position, the mapping from her previous title to her present one is updated accordingly. It is necessary to perform real-time updates on the LinkedIn KG network whenever member profiles undergo modifications or when entities are added. Other companies could similarly take advantage of ML to help them improve their data quality and KGs.
eBay
eBay’s product knowledge graph encodes semantic knowledge about items, entities, and their connections. This information is vital to eBay’s marketplace technology, which automatically connects sellers and buyers. eBay uses KGs to describe products, schedule deliveries, and service customers through virtual assistants. eBay’s KG sometimes links items to real-world entities, establishing a product’s identity and value to a customer.
The KG also links goods. For example, if a person looks for Lionel Messi memorabilia, and the KG shows he plays football (soccer) for FC Barcelona, that person may also be interested in FC Barcelona items or items like signed jerseys from other Barcelona players.
For eBay, understanding product connections is as important as entity interactions, and the knowledge network must answer a search query in milliseconds. Because large graph queries can take hours to complete, eBay engineers built a flexible, universal architecture. The KG keeps track of every entry and change, and the data is organized in a log. This enables a variety of back-end data storage options, such as low-latency document storage and a graph store for long-running analysis. To keep the graph in chronological order, each store adds its operations to the write log, resulting in more consistent results for customers.
Other e-commerce companies could similarly use KGs, leveraging entity relations to better understand their products’ relationships (e.g., suggesting an iPhone case to someone who just purchased an iPhone and successfully modeling various phone sizes and cases in order to offer a case that fits the phone bought).
IBM
Watson Discovery services uses IBM’s KG framework in two ways. First, the framework directly supports Watson Discovery, leveraging structured and unstructured knowledge to discover new information. Second, it allows individuals to construct KGs based on the prebuilt KG. Discovery creates knowledge not present in existing documents or available data sources. Examples include connections between entities (e.g., drug side effects, acquisition targets, and sales leads), new important entities in the domain (e.g., an investor for a specific investment area), or changes in the significance of an existing entity (e.g., an increasing interaction between a person of interest and a criminal). Other companies could similarly leverage KGs to identify prospects, current customers who might be interested in other products, and potential investors.
[For more from the author on this topic, see: “Knowledge Graphs: Harnessing Data to Improve Decision Making & Boost Efficiency.”]