True Natural Language Understanding: How Does Kyndi Numeric Mapping Work?

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Lee Wood
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1 True Natural Language Understanding: How Does Kyndi Numeric Mapping Work? Kyndi is pioneering methods that bring natural language understanding to the enterprise toolkit. Kyndi converts text resources into a sort of knowledge soup which can be used to answer questions and represent know-how for complex subject matter expert models. Knowledge workers using Kyndi technology are more productive, perform their jobs better, and are more capable. This whitepaper provides an introduction to some of the technology underlying Kyndi s Natural Language Understanding. The Challenge The explosion in the amount of text data on the internet and elsewhere presents a nearly insurmountable challenge to knowledge workers. Reading, understanding, and synthesizing so much text data is not humanly possible. Nevertheless, processing the data is a core requirement of many businesses. Today s technologies for extracting knowledge from text are insufficient: Symbolic methods are able to provide deep explanations and model almost any knowledge, but suffer from a tradeoff between the expressiveness of representation and amount of data. For larger amounts of data, the representation is very constrained, often by focusing on keywords alone. Numeric methods scale to massive amounts of data, but are restricted to task focused problems, need large datasets, and are unable to provide explanations. The Kyndi Solution The Kyndi solution builds on years of experience developing AI solutions for private industry and government. In each, we pushed the boundaries of what was possible, and

2 felt that there had to be a better way. After many years of searching, Kyndi found a bridge between symbolic and numeric approaches and has been able to transform the area of natural language processing. Game-Changing Technology Kyndi s technology rests upon the mapping of symbolic to numeric representation for natural language understanding. For those who want a deeper explanation, we provide a brief introduction to three of our breakthroughs: Cognitive Memory Conceptual Relativity The Quanton Cognitive Memory Cognitive Memory forms the core of Kyndi s natural language understanding. Cognitive Memory s core function is to map symbolic graph structures to a numeric representation. With this numeric representation, Kyndi can very rapidly answer questions, recognize similarity, and find analogies. Each of these is driven by the ability to rapidly identify graph similarity within graphs with very large numbers of nodes. Kyndi uses Conceptual graphs to represent knowledge. This is a formal knowledge representation invented by John Sowa (a Kyndi Fellow). CAT: Elsie Sitting MAT Agent Agent

3 History The last decade saw a rapid growth in research e orts devoted to applying graph manipulation methods to natural language processing problems. (One of Kyndi s founders, Paul Tarau, co-wrote the first paper about graph manipulation methods for NLP.) Graph manipulation methods have been challenging to apply at scale due to computational complexity. Cognitive Memory o ers a new approach to address these computational complexity challenges. Using this new approach, Kyndi can represent massive quantities of knowledge in a sort of knowledge soup, which allows for question answering, analogy, inference, and other advanced knowledge induction. What Does It Do? Cognitive Memory creates a bidirectional transition between every graph and a point, or node, in high-dimensional space. Because Cognitive Memory maps each fragment of knowledge to a special location, queries turn into geometric operations. Each new query produces another conceptual graph and can be converted to a point in space. A search for answers then becomes a simple matter of looking for nearby points of knowledge. This technique allows for the application of novel quantum statistical methods in modeling the underlying structure, and hence for rapidly finding structural similarities. Mechanically speaking: Each graph is implemented using the topological framework of generalized combinatorial graphs Each graph is represented by a spectral vector, a series of numbers representing di erent aspects of the graph s structure Each combination of topological framework and spectral vector is paired with a graph contraction algorithm that successively contracts the graph and then captures the

4 new graph spectrum, producing a sequence of spectra for each original graph Together, these functions provide a reversible and highly efficient mathematical representation for symbolic knowledge. Conceptual Relativity In Kyndi s solution, symbols represent knowledge. For machines to understand the symbols, however, a range of background knowledge is needed. For example, consider a sentence about a <CAT> <SITTING> and <LICKING> <HER> <PAW>. A machine needs background knowledge about cats to understand this sentence. The machine needs to know that cats have four legs, that paws are attached to the four legs, and so on. To employ symbolic representations for knowledge in a domain whether the domain of cats or of atmospheric science machines need to have high-quality background knowledge about the domain. This background knowledge is called a domain ontology. Kyndi has an automated method for generating 70-80% of a domain ontology. This method is based on Conceptual Relativity. Why Is The Innovation Needed? The creation of ontologies has been a major barrier to the creation knowledge-based systems. Even today, developing cognitive systems in complex domains can require years of ontology creation effort followed by ongoing maintenance and updates. What Does It Do? Conceptual Relativity automatically generates a proto-ontology from raw background text. It automatically generates a large proportion of a domain ontology, including entities and a broad range of relationships; such as actor, agent, affect, aspect, attribute, context, connect, control, create, depend, disconnect, down, effect, event, instrument, location, manner, method, part, purpose, predicate, perform, patient, source, subsumes, substance, target, up,

5 positive/negative, prototype, and the general relationship is a kind of. Conceptual Relativity is built on a relativistic physical field model in which data is modeled as virtual fundamental particles to produce a semantic distance gauge for clustering and inference. It enables the geography of concepts to be mapped quickly. With Conceptual Relativity, a domain expert can complete a domain ontology in a week, where the task might require years with traditional methods. For more details, see our Conceptual Relativity patent paper, which includes a high-level explanation of Conceptual Relativity and a copy of the patent. The Quanton Today there is a massive, high-stakes effort by physicists to harness the secrets of quantum physics for a whole new form of computing. One reason for this effort is to address very expensive optimization problems that exist across all areas of artificial intelligence (AI). Kyndi has found a way to emulate the results of a real quantum computer for a large class of problems relevant to the Kyndi solution. Why Is The Innovation Needed? The field of AI is littered with problems that, if they were fully computed, would require years, decades, or centuries to complete. Reasoning over language is one of these problems and this is one reason why natural language understanding has been elusive. AI can be seen as the science of finding heuristics, shortcuts, and representations that negotiate the complexity of problems such as natural language understanding.

6 What Does It Do? A type of topological quantum computing (TQC) model called permutational quantum computing (PQC) looks at emulating quantum operations over permutations of a set of data. The Quanton is a method that allows these operations to be performed on a classic GPU-enabled computer, specifically when the implied parallelism is within reasonable bounds. More technically, the Quanton is a general-purpose approximation model to bounded quantum computing, specifically inspired by topological quantum computing (TQC) and the standard quantum circuit model. In the Quanton-based TQC, any computation can be solely defined by permutations. The Quanton serves as an Approximating Turing Machine with computation represented by permutations embedded in a wave function. The wave function is modeled by embeddings of directional geometric probability density functions into manifolds that represent the data or computation geometry. Quanton technology has implications well beyond Kyndi natural language understanding. Areas of potential relevance include almost any optimization problem. Examples include air tra c control, self-driving cars, social network analysis, cyber data analysis, high dimensionality factorially big data analysis, image analysis, and more. For more details, see our Quanton patent paper, which includes a high-level explanation of the Quanton and a copy of the patent.

Chapter 7. Conclusions and Future Work

Chapter 7. Conclusions and Future Work Chapter 7 Conclusions and Future Work In this dissertation, we have presented a new way of analyzing a basic building block in computer graphics rendering algorithms the computational interaction between