AI must be pure, adherence to standards is essential.

AI is not a technology; it is a collection of technologies brought into play as needed.

Cognitive Computing refers to the ability of automated systems to handle conscious, critical, logical, attentive, reasoning modes of thought.


Semantic Computing facilitates and automates the cognitive processes involved in defining, modelling, translating, transforming, and querying the deep meanings of words, phrases and concepts. Semantic Computing is what natural language processing, the heart of cognitive computing is doing.”


Cognitive Computing systems learn and interact naturally with people to extend what either humans or machine could do on their own. They help human experts make better decisions by penetrating the complexity of Big Data.


Cognitive Computing combines multiple Artificial Intelligence techniques, including Semantic Computing, Machine Learning, Natural Language Processing, and inferencing.

Semantic Graphs are a sophisticated database that provides the intelligence for AI.

Where computers have a distinct advantage over humans is the amount of data they can store (and never forget), the much higher speed of their information processing, and the rich descriptions of things provided by Semantic Graphs that are well beyond the ability of a human.


Semantic Graphs mimic some reasoning capabilities of the human brain and offer substantial functional and financial benefits over traditional computing technologies. Our patents.

Human Computer Function
Nervous System Bus Messaging highway
Working Memory Processor and Ram Short-term memory that facilitates planning, comprehension, reasoning, and problem-solving
Semantic Memory Semantic Graph Long-term memory involving the capacity to recall words,concepts, or numbers, which is essential for the use of understanding language
Logical thinking Reasoner Use of facts and evidence to reach a conclusion or solution
Unknown Triples and Ontologies Knowledge is encoded into memory independent of the imput lanaguage it was sourced from. (Multilingual people don't need multiply copies of knowledge)

There are two types of systems involved in delivering Artificial Intelligence.

Machine Learning Systems - Learning-Based AI.

While not intelligent in themselves, these clever techniques (Machine Learning, Natural Language Processing, Deep Learning) are important in extracting (collecting and preparing) knowledge for passing to intelligent systems. More like traditional computing, machine learning systems use man-made algorithms that perform tasks and based on the results, modify the algorithm to improve outcomes.


Machine Learning systems are also useful in “pattern recognition”, where “pattern” refers regularities in data. With training (by humans), such systems can be proficient in repetitive applications such as facial and voice recognition, language translation, and robotics.


Being statistically based, the outcomes are probabilistic, and can be unreliable. Hence, we are now seeing a transition of interest to Knowledge Systems (Semantic Computing) that overcome the probabilistic nature of Learning systems.

Knowledge Representation Systems - Knowledge-Based AI.

There are various types of modern knowledge systems all of which use some type of “graph database” to represent knowledge to a computer.


Graph databases are based on data models that are much more complex than traditional databases and provide a much more enriched representation of data.


The most sophisticated and powerful type of Knowledge System, the one that uniquely facilitates computer intelligence, stores atomic facts in a sophisticated “Semantic Graph” database.


These semantic knowledge graph databases store known relationships and automatically discover unknown relationships between data to represent knowledge in a form that computers can understand by themselves, much like a human does

Don Tonkin, CTO of the Cognitive Software Group (CSG) and former IBM Gold Consultant. 

Semantic Computing is based on the rich RDF data model, it offers the following important advantages over alternative technologies like Property Graph Database solutions. Property Graphs do not facilitate AI and never will. They do not support complex data or complex relationships. They lack the automation of graph databases that is necessary for AI.


There are alternatives to Semantic Graphs known as RDF Graphs and Property Graphs and blends of the two. It is important for potential customers to understand the differences.


Semantic Graphs utilize the RDF data model for representing facts. They utilize the Web Ontology Language, “OWL”, data model for representing knowledge. Facts and knowledge are represented in the Semantic Graph ( often called a Knowledge Graph ) and held in a Semantic Graph database (often called a native “triple store”).


They are open systems enabling the highest level of connectivity for sharing data and information in collaborations suitable for medical research, known as linked open data.


RDF Graphs utilize the RDF data model for representing facts. They utilize an inferior model, or no model, for representing Knowledge. They may be hybrid graphs using an inferior “workaround” approach to “supporting” RDF and OWL knowledge models. They can be open systems for sharing data, but not knowledge.


Property Graphs also utilize inferior knowledge representation models to achieve “ease of use”. They are generally proprietary technologies that lock users in, making escape to a superior graph more difficult and expensive. They often claim to be suitable for Artificial Intelligence; they are not. Management and maintenance of these graphs can become a significant burden and a dead-end street. They are closed systems.


  • Support for automated integration of structured data sources.


  • Support for automated integration of unstructured data sources (text, video, audio) using OCR, Machine Learning, and Natural Language Processing for sophisticated knowledge discovery.


  • Simple (putative) ontologies can be automatically generated from the Semantic Graph data and then enriched using ontology editors.


  • Able to provide for the most complex and sophisticated descriptions of any Graph data model, a mandatory requirement for automated and robust AI.


  • Can update data in existing Semantic Graphs dynamically and quickly.


  • Can discover new knowledge using common Inferencing Engines.


  • Utilize the Linked Open Data standard to link to one unique version of the truth (via URIs/URLs) internally and externally.


  • Provide superior data integration and interoperability with ontologies that define the meaning of concepts, phrases and words across multiple environments.


  • A query language, called “SPARQL”, compatible with multiple internal and external data source technologies removing the need to migrate data before you can query it.


  • Ontologies and RDF used to define knowledge are shared, reusable and editable between knowledge experts, e.g. in medical research.


  • Thirty years of standards setting provide portability of Graph content (including ontologies (rules) and RDF (facts)) between Semantic Graph vendors, protecting your investment.


  • A standardized query language in the style of SQL, further aiding portability, by avoiding proprietary query languages that Property Graphs have.
Features Other Graph Databases Semantic Graph Databases cognitiveAI
Self Managed Data Integrity Extrinsic ✔ ✔
Adheres to W3C standards ✖ ✔ ✔
Reusability ✖ ✔ ✔
Linked Open Data Extrinsic ✔ ✔
Non-proprietary query language ✖ ✔ ✔
Portability to other Semantic Graphs Extrinsic ✔ ✔
Ambiguity ✖ ✔ ✔
Best suited for AI ✖ ✔ ✔
Easier and Quicker to deploy. ✔ ✖ ✔