RuleCNL: A Controlled Natural Language for Business Rule Specifications
Business rules represent the primary means by which companies define their business, perform their actions in order to reach their objectives. Thus, they need to be expressed unambiguously to avoid inconsistencies between business stakeholders and formally in order to be machine-processed. A promising solution is the use of a controlled natural language (CNL) which is a good mediator between natural and formal languages. This paper presents RuleCNL, which is a CNL for defining business rules. Its core feature is the alignment of the business rule definition with the business vocabulary which ensures traceability and consistency with the business domain. The RuleCNL tool provides editors that assist end-users in the writing process and automatic mappings into the Semantics of Business Vocabulary and Business Rules (SBVR) standard. SBVR is grounded in first order logic and includes constructs called semantic formulations that structure the meaning of rules.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language
Most business process automation is still developed using traditional automation technologies such as workflow engines. These systems provide domain specific languages that require both business knowledge and programming…
Language ModelingLanguage ModellingLarge Language ModelVerifiable Checks for Business Rule Consistency
Maintaining consistency between natural language documentation of business rules and their evolving internal implementations is a significant challenge in large-scale systems. We present SIRNA, a tool and framework for c…
Making Sense of Conflicting (Defeasible) Rules in the Controlled Natural Language ACE: Design of a System with Support for Existential Quantification Using Skolemization
We present the design of a system for making sense of conflicting rules expressed in a fragment of the prominent controlled natural language ACE, yet extended with means of expressing defeasible rules in the form of norm…
TranslationTowards Structural Natural Language Formalization: Mapping Discourse to Controlled Natural Language
The author describes a conceptual study towards mapping grounded natural language discourse representation structures to instances of controlled language statements. This can be achieved via a pipeline of preexisting sta…
Business as \textit{Rule}sual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs
Process mining aims to discover, monitor and optimize the actual behaviors of real processes. While prior work has mainly focused on extracting procedural action flows from instructional texts, rule flows embedded in bus…
Benchmarking