Ontologies: AI speaks the language of business
In AI, teaching a machine to recognize entities and business objects across many characteristics, features, properties, and contexts is much like teaching a toddler what a rabbit is. A toddler learns what a rabbit is by seeing what rabbits do, what rabbits eat, how rabbits look, how rabbits behave, and so on. Likewise, ontologies can be constructed to represent multidimensional business objects and rules such as legal contracts, complex accounting rules, complex hierarchies, events, and so on. Imagine a machine that understands the nuances and complexities of the business as well as a human expert.
Starter ontologies provide the backbone for building domain-specific Knowledge Graphs that capture the language, rules, and nuances of the business; these are used to accelerate graph construction and provide a starting point for graph intelligence. Over time, this will evolve to capture all the facets of a business across an ever-expanding network of relationships—revealing hidden patterns among data elements that can be accessed visually. The deeper the graph becomes, the more relationships among data elements are identified, providing richer insights to create new opportunities to connect with customers.
Knowledge Graphs: Connect the dots
One of the core advantages of Knowledge Graphs is that it allows the data to come alive. Data stored in a compact, efficient warehouse are essentially data that are not being used to drive business decisions or to extract business value. On the other hand, when data are projected into a knowledge graph, the relationships, the correlations, the networks, the emerging patterns all come to life rather magically. This allows analysts and decision-makers alike to understand the science of the business more deeply after connecting the dots.
Traditional analytics is forced to consider point of sale receipts, shopper details, grocery lists, and credit card transactions as four separate data sets. A knowledge graph with embedded domain ontologies can reveal essential patterns spanning these data sets in near real-time. Matching a point-of-sale transaction to customers’ historical purchase records and individual shopping experiences can lead to a whole new 360-view of the customer, revealing a nuanced understanding of their individual needs.
Data Scientists used to build models one-at-a-time, individually, which was a painstaking process. Today, with a Knowledge Graph it is possible to run thousands of models and possibilities against the entire corpus of enterprise data sitting in the knowledge graph, using techniques such as swarm AI and Auto-ML. Further, knowledge graph-driven machine learning mimics the elegance of the human brain as it “learns”, “retains” and “connects” ideas together to form new knowledge, leading to an elegant framework for knowledge capture and reasoning.
Monetize graph intelligence in three ways
First, Mastech InfoTrellis’ proprietary Ontology Bank houses a corpus of domain-specific ontologies that can be used to accelerate graph construction projects. Ontologies are the perfect tool for storing knowledge about business objects, entities, concepts, and relationships that can be exploited in several ways:
- To serve as storehouses of domain knowledge that can be used to express complex rules and patterns (e.g., identifying households, resolving multiple and complex business hierarchies, describing intricate patterns/relationships, and recognizing bad data are just a few use cases)
- To serve as a way to include prior domain knowledge to enhance the performance of machine learning algorithms (recommendation engines, decision engines, classifiers, forecasting models, etc.)
- To serve as the backbone of domain-graph intelligence projects (customer and product 360, HR-analytics, financial/operational analytics, and so on)
Custom ontologies can be designed to meet a clients’ exacting needs, following a diagnostic assessment of the problem requirements.
Second, Graph Engineering focuses on developing graph-based data models for a wide variety of applications, including entity resolution, data silo integration, master data hierarchy management, graph intelligence, and advanced analytics applications. Mastech InfoTrellis experts have deep experience in graph database technologies to help you implement your graph intelligence solutions for production applications.
Finally, Graph Intelligence focuses on developing algorithms that support machine intelligence and analytics with varying sophistication levels. Mastech InfoTrellis can design custom ontology-driven KPIs and custom ML inference models for predictive and prescriptive analytics.
Mastech InfoTrellis’ Intelligence Center of Excellence (iCoE) teams have deep expertise in building complex, high-value AI applications that address critical business problems. Hiring talent is not easy, and hiring teams of people with very particular skillsets is a complex and long-term undertaking. Instead, partnering with Mastech InfoTrellis provides access to deep PhD-level expertise in AI to serve the demands of a specific business problem in a scalable way without knowing much about the complexities of building an in-house AI competency. A Mastech InfoTrellis iCoE can work with the business to deliver powerful outcomes, from conception to production, helping realize a significant ROI with minimal risk.
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