As data grows and expands in the digital world, a data layer becomes a valuable asset for organizations. For modern data-driven organizations, more data means more value.
“Any knowledge is added value for any use case. It’s always better to have more knowledge than less. If you’ve got more than you need, you can discard it, but if you don’t have the knowledge, you can’t create it out of thin air.”–Marco Varone, CTO expert.ai
To get the most value out of their data, businesses worldwide are increasingly adopting knowledge graphs for data storage and management.
A knowledge graph is more than just a knowledge base. It is a graphical illustration that reflects an organization’s knowledge base as a digital network of data entities and their relationships. The key features of knowledge graphs include unifying data, integrating data sources, and mapping relationships across the data entities.
Knowledge graphs have proven revolutionary for knowledge management and data analysis. The primary users of knowledge graphs include industries looking to discover meaningful insights from their organizational data. A knowledge graph’s ability to represent corporate data at a web scale makes it capable of delivering excellent business value to global companies. To understand the scale of a knowledge graph’s growth, consider Google’s Knowledge Graph, which has been collecting data shared over the internet since 2012. Today, it holds a collection of 500 billion facts about 5 billion entities.
So what goes on behind the scenes for knowledge graphs that make them operationally beneficial? And what other benefits can we derive from knowledge graphs? Continue reading to find out more.
How do Knowledge Graphs Work?
By now, you know that knowledge graphs convert data into machine-understandable knowledge that makes machines interpret real-world context from data. Let us briefly discuss how that works.
When you gather data for your knowledge graphs, the data comes from different sources in different formats. The knowledge graph uses machine learning and natural language processing (NLP) to extract entities from unstructured data and map relationships across all entities into a schema for the graph. The graph then duplicates the network of entities and their relationships as a data model. The data model provides a simplified view of the complex layers of knowledge. The references to the entities and relationships are stored in a graph database that is a knowledge base for knowledge graphs. Moreover, this data model can infer new relationships at query time.
The contextual capabilities of knowledge graphs enable them to infer new correlations between entities at query time. Therefore, a knowledge graph is more than just a data layer; it is a semantic data layer. It depicts an organization’s data and the categorization and connections of data.
For more background information on knowledge graphs, head to our Primer on Knowledge Graphs to learn more.
In addition to turning data into machine-interpretable knowledge, a knowledge graph offers numerous other benefits. Following are some of the key benefits any modern industry can acquire from using a knowledge graph:
A knowledge graph offers more than just data assembling and accumulation. It is a tool that provides meaningful knowledge management capable of combining real-world data and its associated context from a diverse range of sources. Whether the data is structured or unstructured, in SQL or NoSQL format, a knowledge graph can unify all kinds of data and act as a single source of true knowledge.
Knowledge graphs also act as sources of information for other knowledge graphs. For example, IBM’s knowledge graph framework allows users to build their knowledge graphs using an existing knowledge graph as its base.
The internet houses a sea of information that grows faster than we can sort through it. To benefit from this plethora of information, we need tools and systems that can help us sort through information. Knowledge graphs are an excellent platform for making business knowledge available to all organizational teams so they can collaborate to gather meaningful insights.
Facebook is one of the most popular global organizations with a large user base spread across the globe. To get a complete view of its users and their relationships, Facebook uses a knowledge graph to construct a social graph of its users.
In addition to unifying data and their relationships in a data layer, a knowledge graph effectively reflects real-world data and its complex interconnections. The data network created by a knowledge graph can also accommodate new data and automatically express relevant connections without requiring any rework on the graph.
A knowledge graph extracts entities and context from new data and knows exactly where to fit the new entity in the graph. Once a knowledge graph is programmed, it is an intelligent and flexible system that responds to data changes by automatically updating the knowledge base. There is no need to reprogram the knowledge graph every time a change occurs.
A knowledge graph’s ability to gather information from disparate sources involves data sharing and collaboration. No matter how many touchpoints are involved in building a knowledge graph, knowledge graphs are designed to ensure data security. As the graph grows, its cascading security permissions keep the shared data safe, ensuring secure collaborations between external partners.
Visualization of Knowledge Flow
The data network created by a knowledge graph is an accurate visual representation of the flow of facts between data entities in the graph. The visualization capability of knowledge graphs makes it remarkably beneficial for following business workflows to find problematic areas or discover patterns over time.
Discovery of Hidden Patterns
Modern data-driven organizations use knowledge graphs to solve two significant problems with data’s increasing decentralization: data availability and access. By solving these two problems, knowledge graphs have opened gateways for organizations to sort through their knowledge base at the speed of business and discover hidden patterns in their data.
To test the speed of knowledge discovery on NLP workflows, IEEE experimented on a relationship-mining problem that resulted in DSNAPSHOT achieving knowledge graph analytics of 136Petaflops/s.
By removing a significant bottleneck of sorting through piles of data facts and contextualizing every relation, knowledge graphs allow organizations to skip the broad search and narrow down to their desired solutions.
An overall picture of business knowledge is an excellent start for organizations to get the most value out of their data. Knowledge graphs provide a bigger picture of data entities, their relationships, and the context behind how they relate to other entities and relationships. The visualization aspect of knowledge graphs allows analysts to view workflows sequentially and prioritize their decisions, leading to insightful conclusions.
How can Wisecube’s Knowledge Graph Benefit you ?
Wisecube’s Knowledge Graph Engine is an open-source, AI-enabled knowledge graph that uses ground-breaking AI and NLP to deliver best-of-breed analytics.
Industries today, specifically Biomedical, are dealing with a plethora of unstructured, disjointed data that is difficult to access, hampering research opportunities. With links to a variety of biomedical literature sources and databases, Wisecube’s comprehensive knowledge graph provides the following benefits to the biomedical industry:
- Fusing disjointed datasets
- Providing a unified view of all information
- Surfacing hidden patterns
- Answering research questions
By quickly uncovering important insights, Biomedical researchers can use Wisecube’s knowledge graph to make ground-breaking drug discoveries that can save many lives.
If you wish to get innovative with your data analysis, schedule a call with us today and discover hidden gems in your data.