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In order to support the process on Ontology-Driven Conceptual Modeling using OntoUML, a number of methodological and computational supporting tools have been developed over the years. These computational tools have been aggregated around an OntoUML Editor termed OLED (OntoUML Lightweight Editor). Besides supporting model construction and formal verification, OLED incorporates support for a number of additional methodological features:

  1. A Pattern-Based approach for Model Construction: OntoUML is actually a Pattern-Based Language in the sense that its modeling primitives are patterns, i.e., higher-granularity clusters of modeling elements that can appear in a model only in particular fixed configurations. Moreover, these patterns are of an ontological nature, as they directly reflect the ontological micro-theories underlying the language. OLED supports this pattern-based construction of models. Furthermore, the editor also implements a number of topological patterns that allows for isolating the scope of transitivity of part- whole relations. Finally, the editor allows for the extraction of Domain- Related Patterns from Core Ontologies such that these patterns can be reused for the construction of Domain Ontologies;
  2. A Pattern-Based approach for Complexity Management;
  3. Model Verbalization: Model verbalization stands for the activity of generating a rendering of the model in (controlled) natural language. This process is very useful, for example, to allow domain experts that are not well-versed in the modeling language’s notation, to access a partial view of what is represented in a conceptual model. The editor incorporates a functionality for automatically generating model verbalization in structured English following a slightly modified version of the SBVR (Semantics for Business Vocabularies and Rules) OMG proposal;
  4. Support for the Representation of Domain-Specific Formal Constraints: In order to cover domain constraints that cannot be represented using the language’s diagrammatic notation, the current editor supports the specification of OCL and temporal OCL formal constraints. The editor provides support for syntax highlighting, code-completion and syntax verification (parsing) for textual constraints;
  5. Model Validation: This approach addresses conceptual model validation by using visual simulation. In particular, we have the automatic generation of visual instances (exemplars) of a given conceptual model such that the modeler can be confronted with what her model is actually representing. In other words, the strategy is to systematically contrast the set of formally-valid instances of a given conceptual model (automatically generated by the visual simulator) with the set of intended instances of that model (i.e., instances that represent admissible state of affairs in reality), which exists only in the modeler’s mind. Once the modeler detects a deviation between valid and intended instances (either due to overconstraining or underconstraining of the model), she rectifies the model, for instance, by the inclusion of formal domain-specific constraints;
  6. Ontology Codification: Given a conceptual model representing a domain ontology in OntoUML, we can have different mappings to different codification languages. The choice of each of these languages should be made to favor a specific set of non-functional requirements. Moreover, within the solution space defined by these codification languages, we have a multitude of choices regarding, for instance, decidability, completeness, computational complexity, reasoning paradigm (e.g., closed versus open world, adoption of a unique name assumption or not), expressivity (e.g., regarding the need for representing modal constraints, higher-order types, relations of a higher arity), verification of finite satisfiability, among many others. In the current version of the editor, we have implemented six different automatic mappings from OntoUML to OWL contemplating different transformation styles that were designed to address different sets of non-functional;
  7. Anti-Pattern Detection and Rectification: Given the diffusion of the language, we have managed to assemble a model repository containing OntoUML models in different domains (e.g., telecommunications, government, biodiversity, bioinformatics), different sizes (e.g., ranging from dozens of concepts to thousand of concepts), and produced in different types of contexts (e.g., ranging from academic exercises of novices to models produced by teams of practitioners in industrial or government settings). By using this model repository as a benchmark, we have managed to show that this approach for model validation via visual simulation is not only able to detect deviations between formally valid model instances and intended model instances, but is also able to detect recurrent structures that tend to cause these deviations, i.e., ontological anti-patterns. Once these anti-patterns are catalogued, they were able to devise solution patterns, i.e., solution proposals that eliminate the deviation between valid instances and intended instances. The current version of the editor implements both a mechanism for anti-pattern detection and an implementation of these proposed rectification solutions.

Some Key References

  • J. GUERSON, T. P. SALES, G. GUIZZARDI, and J. P. ALMEIDA, “OntoUML Lightweight Editor: A Model-Based Environment to Build, Evaluate and Implement Reference Ontologies,” in Proceedings of the 19th IEEE Enterprise Computing Conference (EDOC 2015)(DEMO TRACK), 2015.
  • F. B. RUY, G. GUIZZARDI, R. A. FALBO, C. C. REGINATO, and V. A. SANTOS, “From reference ontologies to ontology patterns and back,” DATA & KNOWLEDGE ENGINEERING, vol. 109, 2017.
  • J. GUERSON, J. P. A. ALMEIDA, and G. GUIZZARDI, “Support for Domain Constraints in Ontologically Well-Founded Conceptual Models,” in Enterprise, Business-Process and Information Systems Modeling, Lecture Notes in Business Information Processing, 2014.
  • A. B. BENEVIDES, G. GUIZZARDI, B. F. B. BRAGA, and J. P. A. ALMEIDA, “Validating modal aspects of OntoUML conceptual models using automatically generated visual world structures,” Journal of Universal Computer Science (Print), vol. 16, p. 2904–2933, 2011.
  • B. F. B. BRAGA, J. P. A. ALMEIDA, G. GUIZZARDI, and A. B. BENEVIDES, “Transforming OntoUML into Alloy: towards conceptual model validation using a lightweight formal method,” Innovations in Systems and Software Engineering (Print), p. 17–24, 2010.
  • G. GUIZZARDI and V. ZAMBORLINI, “Using a trope-based foundational ontology for bridging different areas of concern in ontology-driven conceptual modeling,” Science of Computer Programming (Print), vol. 1, p. 1–27, 2014.
  • V. ZAMBORLINI and G. GUIZZARDI, “On the representation of temporally changing information in OWL,” in Workshop Proceedings of the 15th International Enterprise Computing Conference (EDOC 2010), 2010.
  • T. P. SALES and G. GUIZZARDI, “Ontological anti-patterns: empirically uncovered error-prone structures in ontology-driven conceptual models,” Data & Knowledge Engineering, vol. 1, p. 1–50, 2015.
  • G. GUIZZARDI and T. P. SALES, “?Is it a Fleet or a Collection of Ships??: Ontological Anti-Patterns in the modeling of Part-Whole Relations,” in Proceedings of the 21st European Conference on Advances in Databases and Information Systems (ADBIS 2017), 2017.
  • T. P. SALES and G. GUIZZARDI, “Ontological Anti-Patterns in Taxonomic Structures,” in Proc. of the 12th Brazilian Seminar on Ontology Research (Ontobras 2019), 2019.