Are you managing data and analytics at your organization? Learn how tech leaders are using knowledge graphs to transform their approach.
Chances are, you’ve interacted with a knowledge graph today.
Have you searched on Google? Pinned something on Pinterest? Ordered food on Uber Eats or a similar food app? Searched for someone on LinkedIn or Facebook? Asked Alexa a question?
These scenarios are all powered by a knowledge graph. This approach captures and organizes the relationships and associations between concepts, allowing you to optimize data and analytics at your organization.
Reflect user intent with a knowledge graph
A knowledge graph turns your data into machine-understandable knowledge by representing the real-world context that is often MIA. (Everyone has had a bad experience with Siri — sometimes, machines just don’t get it.)
Uber Eats captured user intent by building a knowledge graph into their product. They saw a difference between search queries and orders but recognized that these differences were often the result of the subtle nuances of how human brains connect concepts. For example, what happens when a hungry user searches for udon when dinnertime rolls around?
An eater might search for udon, but end up ordering soba. In this case, the eater may have been looking for something similar to udon, such as soba and ramen, instead of only being interested in udon. As humans, it might seem obvious; udon and soba are somewhat similar, Chinese and Japanese are both Asian cuisines. However, machines have a more difficult time understanding these similarities only based on the textual information. In fact, a lot of work goes into training them to make these types of intelligent decisions on the semantic level. (Via Uber’s excellent blog, “Food Discovery with Uber Eats: Building a Query Understanding Engine.”)
Ignoring this layered knowledge — Which dishes are Asian cuisine? Which dishes are made with noodles? — would lead to an unsatisfying user experience. Humans naturally create layers of associations between entities, leading to a nuanced understanding of a concept. To make this real-world context machine-understandable, Uber built a food knowledge graph that showed the connections between restaurants, cuisines, and menu items. Storing data as a graph instead of as a table showcases the flexibility and expressiveness of intricate relationships between entities. This storage unties the data from the 1:1 relationship typical of tables. Thus, data can be conferred by its meaning to the end user instead of limited by its raw metadata.
A knowledge graph’s data model is unique in its ability to reflect these layers of knowledge as business meaning and infer connections between concepts at query time. With a knowledge graph, when users search for udon, Uber “expand[s] the search to include related terms such as ramen, soba, and Japanese ” automatically. The knowledge graph understands these inferred connections without creating a new schema or pulling new data. This creates tremendous operational benefits in saved IT resources, storage costs, and reduced time spent searching for the correct data, among other things.
When you need a knowledge graph
If you find yourself, your team, or your end-users unable to access the full data required for near-real-time decisions or unable to draw actionable connections or conclusions to keep pace with the ever-evolving pace of business, a knowledge graph might be for you.
A knowledge graph unifies data, creating a flexible data layer that adapts to changing requirements and realities. By easily accepting new data (without any changes to the schema), you can quickly adapt to changing user and market expectations, without over-engaging IT resources.
If you’re evaluating technologies for data and analytics, consider a knowledge graph, especially if your current state matches one or more of the following:
Combining internal and external data: Instead of mastering external data to match internal definitions, simply connect related terms using the knowledge graph’s flexible data model. Power a real-time view of your data.
Complex logical conditions or recommendations: If your analytics processes contain many conditional rules, a knowledge graph will allow you to easily encode and centrally store all business logic. Manage configurations, determine impact across interdependencies, or personalize recommendations based on the built-in AI’s interpretation of conditions in the data.
Fast release cycles with content updates: Inflexible infrastructure can hamper the delivery of feature requests or recurring updates. If data and analytics will be affected by new facts, a knowledge graph offers the flexibility to constantly update your knowledge base without doing any rework. (See News Signals as a Service for a great example here.)
Secure collaboration with external stakeholders: Many innovative solutions require the ability to both collaborate with external stakeholders and allow shared data to remain secure with advanced cascading permissioning capability — a knowledge graph’s inherent interoperability is ideal for these collaborations.
Shared understanding of data across all devices: For IoT applications, a knowledge graph provides edge control and collaboration between proprietary systems and third-party systems and devices. The flexible data model offers quick connectivity of IoT devices for faster commissioning and changes.
Unifying many data formats: A knowledge graph can unify SQL, NoSQL, and unstructured data so that you can truly capture real-world context from all relevant sources. Stardog’s NLP pipeline, BITES, extracts entities from unstructured data like full-text sources and connects those entities and their relationships into the knowledge graph. Most commonly, this is leveraged for semantic search applications that unify data from multiple sources.
Get started building your own knowledge graph
Learn how Stardog’s Enterprise Knowledge Graph platform can streamline access to your data, enrich your data with business meaning, identify new connections and insights, and deliver better data to your existing analytics tools. Watch our on-demand webinar.