Google introduced the term knowledge graph in 2012, referring to a general-purpose knowledge base that would not fit neatly into the tables of traditional databases.
Now knowledge graphs are used in everything from search engines and chatbots to recommendation engines and autonomous systems. Gartner, Forbes, Merrill Lynch, VentureBeat and others have reported that 80 to 90% of the world’s data is unstructured and it’s projected to grow to 157 billion terabytes in the next three years. Pfizer researchers, for example, typically produce between eight and ten terabytes per day.
Data Swamp
Businesses accumulate a goldmine of data in all sorts of formats far less structured than relational databases and spreadsheets. Some firms have nicely organized data warehouses, but most have data swamps — unstructured data lacking even keyword lists or metadata. Information such as supply chain data, financial statements, corporation records, real estate contracts, and countless others exist in these swamps with innumerable document formats.
Digitizing documents and structuring them in spreadsheets and databases creates a semblance of structure, but allows for a margin of disarray. Scanning thousands of pages from paper documents into PDFs, a format that often results in difficulty using indexing engines, is inefficient and allows for human error. Databases with schemas have become receptacles for every form of information that can be digitized, creating an overwhelming data swamp. Not to lose sight of the loss of efficiency in human productivity.
Draining the Data Swamp
Knowledge graphs process an immense amount of complex data and organize it coherently, revealing connections that can be queried to offer insights. The most recent graph engine created by Katana Graph is designed to cut through the monumental data challenge described above while saving energy, resources, and especially time so that businesses can achieve their goals.
Unlike knowledge bases, knowledge graphs contain information about entities with connections described as edges and nodes. These entities are identified and paired with relevant attributes and properties or a description before links between data are established. The type of connections vary greatly and could be direct, logical, semantic, or have other relationships. They can range from associations such as communications between contacts to homophones and connotations of words.
Domain experts create knowledge graphs from differently structured data sources using processes such as automated data validation and integration mechanisms. Knowledge graphs can be assembled from existing but incompatible entities containing disparate data sources in different formats, data types, and locations to create a more thorough and coherent manageable.
Knowledge Graph Industry Applications
To be competitive in the age of information, enterprise companies need to invest in knowledge graphs and to put all their data to work.
Financial Services use knowledge graphs to interpret customer involvement and comply with anti-money laundering initiatives in the financial sector. They create knowledge graphs to assist in preventing and investigating financial crime, enabling banking institutions to understand the flow of money between their customers so that they can better identify anomalies.
Health and Life Sciences organizations use knowledge graphs to organize and classify relationships within medical research. This information helps providers validate diagnoses and identify treatment plans based on individual needs and contributes to quicker solutions in the research field.
In Retail, graphs are used to develop up-selling and cross-selling strategies. Graphs are the cornerstone for promoting products based on individual buying behaviors and popular buying trends across demographic groups. Amazon is a prime example of a well-designed and stunningly successful retail knowledge graph.
The Entertainment industry makes extensive use of knowledge graphs with artificial intelligence-based recommendation engines for content platforms like Netflix, Hulu, and Amazon Prime Video. In this instance, AI learns from online engagement behaviors to recommend content and to facilitate a smoother customer experience.
The insights from graph analytics guide business leaders in making more informed, strategic decisions.
To compete and innovate, organizations must harness the most advanced data technologies to rapidly identify high-potential opportunities and to maximize their R&D efforts. Katana Graph’s graph intelligence platform delivers game-changing graph analytics and machine learning for the most mission-critical use cases. These capabilities make it possible to create richer knowledge graphs more quickly, greatly improving the insights and opportunities uncovered from your organization’s data. Click to schedule a meeting.
