What Is a Knowledge Graph?

Leveraging a knowledge graph helps employees and customers gain insight into your company and its products. What is a knowledge graph? Yext tells you about it here.

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When customers start looking for information about your brand, they may not know the specific keywords to use. They'll have a question or a product that they want more information about, and they'll enter their inquiry as it comes into their brains. Make sure that they can still find the answers they're looking for quickly by using a knowledge graph to help provide the best search results.

Let's explore the knowledge graph and why it's so important for your company to have one.

What is a Knowledge Graph?

The future of search engines is evolving with artificial intelligence (AI) playing a larger role. Knowledge graph databases are linked data sources of structured data for facts about your brand. Whether it's new data about a product offering, store location, job opening, or professional credentials, you'll be able to provide people with actionable answers to the questions they're already asking. A knowledge graph works by linking datasets to provide context for products, services, concepts, and brands.

The linking combines entities to create ontologies that allow users to access information across the spectrum as well as analyze new knowledge for deeper insights. One example of how a knowledge graph works through extrapolating information is to look at 'when is Y brick-and-mortar store open San Diego.' Currently, there are 78 cities in the world with the name San Diego, but there are only two places in the United States.

Within a knowledge graph, nouns are classified as entities like person, place, or thing. Y store and San Diego are the two entities in the query. Most of these entities will have multiple subclasses that overlap with each other. For example, Y store has an address in San Diego, California which means that they both are related to the company Y. These relationships allow a user to find out more information about their search without needing to specify everything in the query.

Based on other information like the primary language of your website or the zip code, a knowledge graph can conclude that your store is in the United States. One San Diego is in Texas and the other is in California. Based on the sizes of the respective cities and the larger notoriety of San Diego in California, a knowledge graph would likely infer that your store is located in California, even if the user doesn't explicitly say so.

Knowledge graphs refine search results so that people receive accurate and relevant information in response to their queries. By drawing on existing relationships of entities, like the relationship of California to San Diego, the results page can be more precise about offering information such as store hours in Pacific Standard Time.

It could also look at your website, which mentions the ocean and surfing, which also adds credence to the California assumption. Then, when people look up the location of your store or ask 'where is X store?' they'll see that it is in California. Of course, a knowledge graph can show more links and inferences than just location.

How Can a Knowledge Graph Help?

When a user searches your site, they'll enter their search in natural-language terms. Natural language processing is one of the machine learning models employed for knowledge graphs. These models learn to understand multiple vocabularies and can deliver semantic web search results due to attributes and identifiers you provide your knowledge base.

The result is, when a customer searches for a product on your company's website, they get relevant answers on the search engine results pages (SERPs) no matter how they enter their query or its complexity. These SERPs might include a link to purchase the product itself, related products, products that work in conjunction with the one searched, and blogs that explain more about the product's features.

A good knowledge graph forces good SEO (think Google search because of the Google knowledge graph). So, a good knowledge graph does two important things for your users: Increases their average use cases by introducing them to related products or products frequently bought in conjunction with the original search term and reduces the number of calls or emails to customer service.

When consumers have access to FAQs, tutorials, and blogs explaining the available features, innovative ways to use a product, or common troubleshooting tips, they won't need to contact an agent for assistance. This saves your company time, energy, and money since the people who will still contact your agents are those with particularly challenging issues that require human expertise.

Knowledge graphs also record information about the types of questions your customers are asking and knowing what your consumers want to find, like trending topics, can help you make data-driven decisions to create and include more content in the future or analyze which products are commonly purchased together.

It can also check your stores based on the customers' IP addresses to see where they are located so that they'll get information about how your stores, inventories, or policies vary across regions. For example, Netflix offers different movies and TV shows or the same TV shows or movies in different languages, based on your location by employing a knowledge graph.

Another way that knowledge graphs are used can enhance your customer service. When customer service representatives don't know the answer to a question, they could put the person on hold while they look up the information by hand or transfer them to someone else who does know, but most people become very frustrated when they've bounced around between agents.

With a knowledge graph, the employee could easily find the answer without needing to hand off the reins to someone else and keep the customer from becoming frustrated. It's important to know that knowledge graphs need people to maintain them and keep them up to date.

The point is this; a knowledge graph allows your company to collect and deploy information to customers in a meaningful way. With a knowledge graph, your search engine can interpret people's intentions with their questions instead of simply searching each word in a vacuum. Understanding phrases and how words modify entities is a major component of ensuring relevant and accurate search results.

Inside the Knowledge Graph

A knowledge graph has a few basic components that combine to link all of your brand's data together. It needs a database to store information, a graph to analyze data and find links through semantic metadata, and a base of knowledge with basic facts and the ability to extrapolate.

Using the earlier example about the store in San Diego, the knowledge base would tell the knowledge graph where cities named San Diego exist, which primary languages are spoken in those areas, and how to link that data together to draw conclusions.

Knowledge graphs work by utilizing machine learning algorithms to connect the dots between various data points automatically. It also works in tandem with other databases you use inside of your company to help keep everything straight, whether you're working with structured or unstructured data.

With a knowledge graph, you can input specific custom fields and entities that you want it to manage so that you can keep track of what information is publicly available for people to find. You can also create new entities to encompass nouns that aren't currently included in out-of-the-box knowledge graph builds to ensure that all of your company's relevant pieces of the puzzle are being monitored.

If your knowledge graph isn't drawing the right connections between your entities, you can also input relationships to help teach your knowledge graph about connections and where to look for them in the future. This is where the machine learning aspect comes into play. By showing your knowledge graph where to look and how to find relationships with some of the starting dates, it will be able to do so on its own moving forward.

Using a Knowledge Graph

There are plenty of ways to use a knowledge graph for your customer's and employees' convenience. One of the most common uses for a knowledge graph is to ensure that your potential customers get the facts about your company when they're doing their research. You can help your customers find where to go to buy your products or services as soon as they search for them to make their searching more efficient.

Another good thing about the knowledge graph is that you only need to make changes once. As soon as you have demonstrated what needs to be changed, the knowledge graph will automatically update your linked entities to reflect that change. The graph can even find relationships between the new product and existing entities.

In Conclusion

Having a single source of truth for public information about your brand is a great way to stop competitors proactively and unhappy customers from spreading false rumors about your company. It also clears up the confusion that potential customers might have when they're deciding whether or not to give you their business and is key to improving your conversion rate.

Contact us for more information about how having a knowledge graph can make it easy for people to learn the truth about your brand.

Sources:

What is a Knowledge Graph? | Ontotext.com

What is a Knowledge Graph – Transforming Data into Knowledge | Poolparty.biz

What is a Knowledge Graph? | IBM.com

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