Abstract
Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component objects and their states (attribute values) at different levels of granularity. This paper provides some significant contributions to computer science. It extends a fuzzy vector space object-oriented model and method (FVSOOMM) to present a new platform and a method guideline capable of designing objects and attributes that represent timepoint knowledge objects. It shows how temporal case-based reasoning can use distances between temporal fuzzy vector functions to compare these knowledge objects’ evolution. It describes examples of interfaces that have been implemented on this new platform. These include an expert’s interface that describes a knowledge class diagram; a practitioner’s interface that instantiates domain objects and their attribute constraints; and an end-user interface to input attribute values of the real cases stored in a domain case database. This paper illustrates resultant knowledge bases in different domains, with examples of pulmonary embolism diagnosis in medicine and decision making in French municipal territorial recomposition. The paper concludes with the current limitations of the proposed model, its future perspectives and possible platform enhancements.
Highlights
IntroductionExtension of the TFVS model This paper extends the temporal fuzzy vector space (TFVS) model with the different steps of the method within fuzzy vector space object-oriented model and method (FVSOOMM)
- Step 3 of Figure 3 concerns the knowledge design with a fuzzy vector space. It relies on the composition relationship and the qualifier attribute descriptors (QADs) in Figure 5 to describe the characteristics of each attribute, which should be used in at least one object type descriptor (OTD) in Figure 6 to define the structure of the necessary knowledge object instances
Among the contributions presented in this paper, two important contributions are the temporal fuzzy vector space (TFVS) model, which is a fuzzy object-oriented model that allows us to design the values of the attributes and the behavior of the objects of the system over time, and the detailed methods in fuzzy vector space object-oriented model and method (FVSOOMM) that describe the necessary steps to design the TFVS from a UML class diagram representing the domain ontology
Summary
Extension of the TFVS model This paper extends the temporal fuzzy vector space (TFVS) model with the different steps of the method within FVSOOMM. This provides a new mechanism to model knowledge bases in many different and complex domains, and to capture the semantics of the relationships between the objects and actors described in the ontology of their knowledge domain. In common with other authors [6,7,8,9], the present authors trust that object-oriented approaches offer a more general model of knowledge representation than just from fuzzy logic alone
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