These examples are all instances where humans learn using rules and relationships. This new information is stored in memory and enable humans to carry out their daily lives.
Similarly to humans, computers can learn how to achieve certain outcomes using rules and relationships, with advanced semantic reasoning technology. In brief, semantic reasoning is the process of making “logical deductions from the information that is explicitly available”. The set of deductions we will make is encoded in rules, which can be written in the Datalog rule language.
“I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain.” Geoffrey Hinton
By encoding knowledge, such as domain expertise, rules and ontologies, we can determine the relationships between the stored data. We can then use this knowledge as a basis for scientific study or modern-day applications.
We can understand the structure of food chains and other datasets, using ontologies and hierarchies. Similarly, to learning language or how to drive, we can encode the rules for word and tense formation, or the rules of the road, into the system. We can include logic and therefore determine the compatibility of different lightbulbs with the fitting. This can be extended to commercial scale compatibility assessments, answering questions like “What components do I need to pick in order to assemble a machine?” or “Which products should I show to a user of my website?” The use cases are endless.
RDFox is a semantic reasoning engine and a high-performance knowledge graph which “enables intelligent information processing by providing means for representing and reasoning with domain knowledge in the form of rules and ontologies”.
Datalog rules can be used to link data sources, verify data sets, and improve the richness of a graph database. For example, if I told a human that I live in Oxford, they could determine that I live in England, thus in the UK. However, when a normal graph database is asked if I lived in the UK, it would say no — as inside the database it says I live in Oxford. Although humans know Oxford is a city located in the UK, if the relationship is not explicitly stored in the system, it won’t know the link.
“Unless in communicating with it one says exactly what one means, trouble is bound to result.” Alan Turing
Similarly to how humans think, in RDFox we can add a rule, and as a result RDFox will interpret the relationships between myself, Oxford, Oxfordshire, England and the UK to deduct that I do in fact live within the UK.
This is a simple example of semantic reasoning. However, imagine we are talking about a database containing thousands of people, some of which are listed as living in Oxford, some in Oxfordshire, some in England. In order to say that they are from the UK in SQL, this already counts as three queries (one per layer of generalisation).
Now imagine doing this for another relationship, such as the job people have: Some may be listed as Paediatric Heart Surgeons, some as Paediatricians, some as Surgeons, some as just Doctors. Now if you want to return all the Doctors in the UK you already have to run 12 different SQL queries. Add 10 more relationships and you have a nightmare.
With Semantic Reasoning on the other hand, this can be expressed with one rule only, which allows us to use only one query. And (at least in RDFox), semantic reasoning is done incrementally, meaning that if the data changes (say one of the surgeons retires), then our conclusions change too, automatically and in real time.
Semantic reasoning becomes extremely powerful when the combination of domain expertise, ontologies and rules, including rule hierarchies, are embedded into the system.
In a business setting, it is common to need expert knowledge to deal with business questions, from optimising strategy through to product development and analysis. This is often a costly and time consuming endeavour. However, using semantic reasoning technology, the expert knowledge can be embedded into the system, granting it the gift of intelligence, as a human would have.
Looking at our example of determining compatibility of components in an industrial configuration setting, the logic of compatibility is added to the system using rules. The expert knowledge then exists within the system and is accessible by the end user, on demand. This allows the system itself to provide compatible solutions, accounting for user specifications — the intelligent application thinks more like a human would think, and a lot faster than a human would too!
By storing information as richly connected entities within knowledge graphs, we drastically improve the ability of semantic reasoning technology to derive logical inferences from the data stored within. The structure of information within RDFox, as well as the immense power of the reasoning engine itself, offer organisations a unique opportunity to embed domain knowledge within their graph database. The result is the creation of truly intelligent applications, which can operate in real-time and with the same conviction as humans.
To learn more about RDFox visit our website or check out our medium publication. To try RDFox yourself, you can request a free 30 day trial license here. To request a demo, contact us at [email protected]