Semantic Search for the Enterprise powered by a Knowledge Graph

Dec 16, 2020, 3 minute read
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Having the right information at your fingertips is critical, whether you’re a computational biologist evaluating relevant proteins to identify a promising drug target, a customer service representative troubleshooting a problem, or a manufacturing executive looking for the most readily available replacement part. In each scenario, the information required to solve these problems is spread across different systems and lives in a variety of formats. As a result, people wind up hunting down information across various systems — wasting significant time — or sacrificing untapped potential because they lack sufficient inputs to make the best decision. 

Search applications have emerged as the de facto solution to this problem, promising to unlock access to information. For internal search applications, arming employees with the right information saves time and lets experts do their job better by reducing blind spots and preventing duplicate work. For products with built-in search, providing the right information to customers increases user satisfaction and engagement. 

But as search tools have become prominent, user expectations have also increased. The tech titans have dictated consumer expectations for finding the right information — and fast. Studies show that slower search experiences lead to user churn, even with increases in response time as small as 100-400ms

Chatbots and voice assistants like Alexa are also changing user behavior — now users are expecting a single correct answer from their AI. (Not living up to these consumer expectations can have consequences.) Additionally, voice search leads to more conversational requests, which can confuse even an advanced search engine. 

While there are plenty of enterprise search options on the market, they’re failing in the face of this increasing complexity and performance demands. Traditional enterprise search solutions face a tradeoff between precision and recall. Increasing precision may leave out potentially relevant materials. Prioritizing recall often returns an overwhelming number of results, many of them irrelevant. Consequently, time and energy is wasted reviewing irrelevant answers or testing different search terms in hopes of getting better results. Or worse, decisions may be made with incomplete information. 

The future: semantic search for the enterprise

In contrast to typical enterprise search solutions, Stardog’s knowledge graph-powered search returns fewer, more relevant results, reducing time spent searching up to 90%. A knowledge graph improves search by capturing the meaning of the search terms. For this reason, knowledge graph-powered search is often called “semantic search” — search enriched with meaning. Essentially, semantic search operates by representing the layers of connections between various data sources. By representing these myriad relationships, the search engine is able to operate similarly to how humans think — organizing information through associations and hierarchies.

“Today, many search engines utilize multiple knowledge graphs in order to go beyond basic keyword-based searching to understand concepts, entities and the relationships between them.”

Gartner “How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications,” Afraz Jaffri, 27 May 2020

If you’re unfamiliar with knowledge graphs, you might be surprised to learn you probably use them everyday. Google Search is powered by a knowledge graph that contains 500 billion facts about five billion entities. Amazon’s Alexa uses a knowledge graph to help return a single perfect answer. Uber Eats’ knowledge graph helps people find the exact food they want as effortlessly as possible. The list goes on — Pinterest, LinkedIn, eBay, and more all use knowledge graphs. Need a primer on knowledge graphs? Be sure to check out our page here

Interested to learn more about the benefits of bringing semantic search to your organization? Download our whitepaper to learn how the world’s largest organizations are increasing productivity by empowering their employees to spend less time searching and more time discovering knowledge and insights.

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