Probably not many people know that when OpenRules was founded in 2003, we initially positioned ourselves as a Semantic Web company. However, absence of practical results at that time forced us to limit our ambitions to Business Rules Management. As a result, we created an open source product that allowed business analysts to represent their business logic and then a rule engine effectively executed business rules – these capabilities quickly brought tangible values to our customers. Over the years, OpenRules, like other popular Business Rules and Decision Management products, was successfully used by many companies worldwide to build practical decision-making systems.
These days business analysts work in concert with developers to successfully develop so called “decision models” capable to solve their own business problems. In most cases they represent domain-specific knowledge by defining:
a) Business glossaries as a set of decision variables with their various characteristics
b) Business rules (such as decision tables) to represent the relationships between decision variables.
Then a rule engine executes the decision model to find an answer to a certain business question, e.g. calculate insurance premium or determine loan eligibility. The last 15 years resulted in serious progress in creating user-friendly interfaces for decision modeling. Now we even have the first standard “DMN – Decision Model and Notation”. Probably the most important achievement is the fact that the objective to move control over business logic from IT to subject matter experts became a reality.
However, today’s decision models force human experts to describe almost ALL relationships between decision variables using business rules which may become very complex and include various calculation formulas, filters, iterations, etc. How to minimize human involvement?
Some good practical results already came from the integration of the Business Rules approach with:
- Machine Learning (Predictive Analytics) to automatically generate rules based on the historical data
- Decision Optimization (Prescriptive Analytics) to apply an optimization engine to explore relationships not covered by business rules and to find not one but multiple alternative solutions, and even optimal solutions.
However, to further automate decision making across multiple inter-connected decision models we need to move business and decision management to the Semantic Web territory and try to integrate the results produced in the both fields. There were interesting initial inter-communications between DecisionCAMP and RuleML+RR in 2016 and 2017 when people from both communities attended presentations during both events. I also would like to mention the following related papers:
- RuleML – DMN Translator by Adrian Paschke, Simon Könnecke
- Semantic DMN: Formalizing Decision Models with Domain Knowledge by Diego Calvanese, Marlon Dumas, Fabrizio M. Maggi, Marco Montali
- Combining DMN and the Knowledge Base Paradigm for Flexible Decision Enactment by I. Dasseville, L. Janssens, G. Janssens, J. Vanthienen, and M. Denecker
- Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Object-Relational Logic by Gen Zou, Harold Boley, Dylan Wood, Kieran Lea
- Development of the Rule Based Approach to Traffic Management by the Dutch Road Authorities by Silvie Spreeuwenberg, Rolf Krikke
And finally, I want to share my answer to the question asked during DecisionCAMP-2017 Q&A Panel: What is the next “killer” application for Decision Management?
“We probably should look for an answer at our co-located conference RuleML+RR where Semantic Web people already have created good reasoning tools. If we consider our decision models as ‘decision ontologies’ and apply similar reasoning tools to them, we would not need to force our customers to write specific rules that lead to calculation of insurance premiums or acceptance/rejection of loan applications. Having rules to define only relationships inside our inter-related decision models (ontologies), they (users) would only need to specify the goal (e.g. insurance premium), and then a Decision Reasoner will automatically calculate it for a particular insurance policy.”
Looking forward to more suggestions for a productive cooperation between experts in two related fields: Semantic Web and Business Rules&Decision Management.