The AI-Powered Enterprise. Seth Earley
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they need it. Companies that do this will gain an edge in the AI future, while other companies will suffer competitive losses. Organizations cannot gain this edge and do so successfully, cost-effectively, and at scale without investing in the foundation of correctly designed and applied ontologies.
Why is it important to have a framework for organizing information? It can provide a competitive advantage based on how you envision your business, and you can differentiate your company by anticipating what your customers will need and by doing some of the heavy lifting for them. If your company is dependent on an individual for facilitating customer interactions, developing an effective ontology can buffer the impact if the individual leaves the company, or it can help you scale up to reach more customers.
In the rest of this chapter, I’ll show you more about why ontologies matter, what they include, and how to build them.
UNDERSTANDING ONTOLOGIES
The term ontology refers to a domain of knowledge and the relationships among different concepts. The idea originally comes from philosophy, where ontology is the study of the nature of relations and being.
An ontology allows people and systems to understand relationships between concepts, just as a reference librarian can guide someone looking for information at a library. Someone seeking information may not know that valuable information about a subject can be found in a particular section of the library, or they may not know the best place to locate information that is less commonly available. This is similar to a book index, in which the “see also” entries guide the reader to another location that they might not have otherwise found or looked at.
Ontologies are built up from taxonomies. A taxonomy is a clearly defined hierarchical structure for categorizing information. Most people are familiar with the taxonomic classification system for animals, which includes invertebrates and vertebrates, and within the vertebrate category, birds, fish, reptiles, amphibians, and mammals. Similarly, the Dewey Decimal System is a taxonomy for categorizing books.
Taxonomies and ontologies have different purposes and uses. On a website for home products, a taxonomy could classify different departments such as lumber, tools, lighting, and appliances. Within each category, there could be subcategories such as hand tools and power tools, or refrigerators and stoves. In many cases, a taxonomy like this is sufficient for prospective customers to locate the items they need. However, a different level of sophistication can be achieved by overlaying this information with an ontology that adds richer information on products. This step would be required to respond to answers to questions such as “What are you trying to do?” If the answer is “Build a deck,” then the store could present a suggested list that included supporting posts, decking lumber, deck screws, and a circular saw. Ontologies are a key ingredient for personalization and proactive marketing.
Ontologies Power Meaningful AI Capabilities
Ontologies begin as a holistic understanding of the language of the business and the customer, and are then designed into processes, applications, navigational structures, content, data models, and the relationships between concepts. They contain language variations, alternative spellings, translations, acronyms, and technical terms. They can describe “is-ness” and “about-ness”—this is a contract, it is about a services engagement, it is also about this vendor, for example. Ontologies can also support advanced capabilities to drive intelligent virtual assistants (bots). They can form the basis for inference engines—mechanisms to essentially answer a question that has not been preprogrammed into the bot. Bots powered by ontologies are faster to deploy, more scalable, and more cost-effective. Every aspect of business requires contextualized knowledge. The role of AI is to use the ontology to assist with this contextualization.
Here’s an example scenario. Imagine a bot that answers questions about support problems.2 For this to work, the bot needs to understand “intent”—the thing that the customer is trying to achieve. According to P.V. Kannan’s AI book The Age of Intent, intent is the fundamental concept that drives all intelligent virtual agents. As Kannan defines it, “Intent is a determination of what a customer wants from an interaction with a company.”3 Once an AI determines that, it can supply an appropriate answer.
For example, a customer may not be able to connect a printer to the network. The troubleshooting bot can determine the customer’s intent by first extracting the elements of the problem from the customer statement (called the utterance). “I cannot connect my printer to the network” contains the entity “printer” and “network” and the symptom “cannot connect.” These elements are captured in the ontology along with synonyms and variations such as “unable to connect” or “not connecting.” The intent, which the bot determines from this information, is “fix printer connection problem.” Having determined the intent, the bot can walk the user through steps to solve the problem.
There is also a relationship between the symptom and a solution. The solution may require more information, which the bot can request from the customer. If the customer is logged in, the bot can look up information from the list of equipment they own. With this knowledge, the system can better identify and present the appropriate troubleshooting steps.
Each of these elements and the relationships that associate them form the knowledge graph—that is, a knowledge structure that contains related elements. You may be familiar with graphs of relationships from Facebook, where you may find new friends via different connections and follow those connections to another group of acquaintances based on factors like school, group membership, or interests. The movie database IMDb shows a similar set of graph relationships—you can choose a movie and look up the actors and directors to see which other movies they were in and which other actors they costarred with (useful for the game Six Degrees of Kevin Bacon4). The ontology becomes a knowledge graph for your organization, with the ability to answer a limitless number of questions over time.
How Ontologies and Taxonomies Worked at Applied Materials
In the case of Applied Materials, the taxonomies described every aspect of the chip manufacturing process, including these concepts:
| Account | Geography | Partner | Solution |
| Application | Code | Platform | Status |
| Assembly | Security | Process | Subject |
| User | IP | Equipment | Technology |
| User Interest | Owner | Product | Units of Measure |
| Division | Language | Published To | Substrate |
| Document Type | Business Unit | Region | Sub-assembly |
| Plant | Configuration | Severity |
(Representative list of vocabularies. Concepts and names changed or omitted for confidentiality.)
More than 30 taxonomies described Applied Materials’ world, and each taxonomy had a relationship to others—for example, Platform to Process, Assembly to Product, Partner to Region, Solution to Plant, or Severity to Status. These knowledge relationships could be mapped across content processes, allowing automated “reference librarians”—AI—to suggest resources and answers. These embedded relationships enriched the ontology to create a conceptual representation of a domain of knowledge. Taken together, all these taxonomies and all of their tens