Knowledge-based Artificial Intelligence. That’s the direction taken by startup Cognonto, co-founded by Michael Bergman, a man whose history in the AI, Machine Learning, Semantic technologies, Internet search and data arenas goes back a long way. That includes his additional duties as CEO of Structured Dynamics, birthplace of UMBEL (Upper-level Mapping and Binding Exchange Layer), a knowledge graph and vocabulary for interoperating Web-accessible information, which had its latest update in May.
As far as the new Cognonto venture, whose initial fruits are the Cognonto Platform and KBpedia knowledge structure, Bergman says it’s been in gestation for about eight years.
“The ‘aha’ moment came when we realized how many of the large-scale QA systems were basing their knowledge structure around Wikipedia,” Bergman says. “We realized this was a huge storehouse of very useful information, but one that everyone reinvented every time they brought in their own system,” from Siri to Viv to IBM Watson and the Google Knowledge Graph.
What could serve better specifically to support Artificial Intelligence apps was systemizing the organization of critical knowledge bases – the very often leveraged Wikipedia, Wikidata, GeoNames, OpenCyc, DBpedia and UMBEL – into a single structure. Instead of taking a bespoke approach, Bergman envisioned creating a resource that could be shared by multiple companies.
“We had done a lot of Wikipedia mapping work and Semantic Web work for a number of years and this gave us a real focus,” he says. “If we were purposeful around this, we could create a resource to make AI apps a lot faster and more efficient.”
Bergman and his team began more purposely seeking customers for what would emerge as the Cognonto venture a few years back, focusing on those companies who needed knowledge-based applications and were more closely aligned to its R&D interests. “I think our customers were evolving as we were,” says Bergman, looking to solve particular knowledge problems with a Semantic approach but requiring knowledge bases underneath all that to be productive.
“We were rapidly moving to more knowledge-oriented apps to do things like entity recognition and tagging and categorization” – things that at the time it was viewing as more Semantic technology-based. “But really the key thread in all this is that they also were knowledge-based,” he says. There was a growing realization, he notes, that it was the knowledge component matched with Semantic technologies that then leverage this ability to do knowledge-based AI.
New Mindset for a New Solution
KBpedia is the knowledge structure component that combines the six major knowledge bases mentioned above – with their hundreds of thousands of concepts and 20 million entities – plus mappings to another 20 knowledge bases. Governing this all is a knowledge graph, or schema, that Cognonto calls the KBpedia Knowledge Ontology (KKO).
Bergman says that there was no adequate off-the-shelf way to bring together the various knowledge bases that it needed to. He was led to the work of a 19 century American mathematician, philosopher, and polymath, Charles Sanders Peirce, one of the first individuals to formulate the logic of signs. Bergman was impressed by his approach for organizing how to think about upper-level ontologies and it’s in large part the genesis of KKO’s ontology.
“There is a firm philosophical logical grounding to the system,” he says, helping to deal with some hard nuts to crack around modeling issues. There are many different approaches for distinguishing a logical basis for ontologies, but Peirce basically says to base everything around 3s, explains Bergman. That is, the object itself; what a particular agent perceives about the object; and the way that agent needs to try to communicate what that is.