It’s 2018 and companies need to improve their understanding of customers, vendors, employees and investors. Companies need many more ears and mouths to absorb and disseminate knowledge. Machines engaging with humans in productive conversations using Natural Laguage Processing is no more a dream as well demonstrated by Siri, Alexa and Google-Home! Why is this so important? Obviously because it would empower companies to do what they need to do this year. But before we get to it , let us pull back and take a look at what makes knowledge transfer hard.
Tacit and Explicit knowledge
Tacit knowledge is what we have internalized and that is what we really use when we make a number of decisions in our personal and professional life. A pilot uses his tacit knowledge and has no use for explicit knowledge such as an instruction manual while flying a large passenger aircraft. Doctors, lawyers, architects and soldiers owe their tacit knowledge to years of training alongside their seniors. This tacit knowledge transfer happens while socializing as per Alex Pentland in his famous book Social Physics
Explicit knowledge is what we externalize and express in English or some other language in articles, books, videos, podcasts, databases and spreadsheets etc.
Nonaka and Takeuchi have developed the SECI model for organizations to grow their knowledge-base by cyclically going through the following 4 steps that looks like an ever expanding spiral
- Socialization ( Tacit to Tacit)
- Externalization (Tacit to Explicit)
- Combination (Explicit to Explicit)
- Internalization (Explicit to Tacit)
Tacit knowledge is hard to externalize. No one can communicate all he/she knows by writing it down. On the contrary socialization is easy. It happens easily because of the social setting which provides the context. Human brain snacks on information that is in context. On the contrary most of the communication directed at you on the internet is sheer noise due to lack of context. Wouldn’t it be wonderful if chatbots storing a lot of explicit knowledge could use it in a virtually social setting to engage in conversations with us?
Let chatbots do the hard work of externalization
Imagine having a friend by your side who has the patience to answer all your questions that you need to be answered to understand and participate in a conversation. All of us were very inquisitive as children. We stopped asking questions when we saw adults around us losing patience.
Chatbots can simultaneously engage in thousands of conversations. Socializing with chatbots makes it easy for us to weave bits and pieces of knowledge in the right context. This knowledge is provided accurately in the right context by a chatbot that is lurking the background while we are going about our daily routine of using our enterprise software or our favorite app. Explicit knowledge is internalized by a chatbot in a few seconds. But for chatbots to learn new pieces of knowledge , they need to ask questions and plug incoming pieces of information in the right place. Ontologies provide the underlying infrastructure for chatbots to organize knowledge – somewhat parallel to the way humans learn by organizing related facts in the same part of the brain.
Building an ontology made easy!
An ontology is a little more than a taxonomy which is a tree like structure that organizes entities in classes, subclasses and instances. In addition it also establishes connections between leaves of the tree by specifying one way, two way and transient relationships. Its best represented in a graph database such as neo4j. Organizing the knowledge specific to your company’s business domain in a graph database is a daunting task. Many companies have spent years building ontologies. No doubt that it’s an effort that more than pays for itself- but the sheer volume of work deters companies from investing in this project.
Synerzip has developed a semi-automated way of building ontologies. The system takes documents relevant to your company’s domain – which may be catalogues, brochures, articles, emails, spreadsheets, resumes or handbooks and puts them in a corpus. Top few hundred the frequently occurring terms in the corpus are manually organized by an subject matter expert to form the seed ontology. This might take a few days or weeks at the most.
Automation kicks in once you have a reasonably well populated seed ontology. The program computes similarity scores based on the context in which terms appear. Terms having high similarity are put in the same class. There are still some places where a human expert is consulted to resolve contention. But it makes the project more feasible and viable for companies planning to build their ontologies from scratch. For companies who already have built their ontologies , this system could be a good way to update and improve utility.
Building chatbots has been made easy by providers like Facebook messenger, Slack and Skype. By the end of this year , we will see chatbots being preferred over mobile interfaces by many applications.