Fuzzy Data Types Research Articles

Fuzzy ontology datatype learning using Datil

Semantic Web technologies (especially, ontologies) are very important nowadays in intelligent applications to represent the important knowledge in an application domain. Shortly after the birth of ontologies, the inability to adequately handle knowledge affected by imprecision or vagueness, which is inherent to many real-world scenarios, was observed. Fuzzy ontologies were proposed and developed as a solution. However, the problem of building fuzzy ontologies has received little attention in the literature. In fact, there are few examples of publicly available fuzzy ontologies, suggesting that new techniques helping users to build fuzzy ontologies are needed.In this paper, we develop advanced strategies to learn fuzzy datatypes for fuzzy ontologies. Our approach uses real-world data and unsupervised clustering algorithms to compute the fuzzy datatype definitions in an automatic way. Our learning algorithm is implemented in the Datil system, a tool supporting different input formats and devices, including a mobile version. Our approach could be orthogonal to other existing approaches learning fuzzy subclass axioms.

An Extended Semantic Interoperability Model for Distributed Electronic Health Record Based on Fuzzy Ontology Semantics

Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a separate standard ontology is created for each input source. Second, a unified ontology is created that merges the previously created ontologies. However, this crisp ontology is not able to answer vague or uncertain queries. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. The used dataset includes identified data of 100 patients. The resulting fuzzy ontology includes 27 class, 58 properties, 43 fuzzy data types, 451 instances, 8376 axioms, 5232 logical axioms, 1216 declarative axioms, 113 annotation axioms, and 3204 data property assertions. The resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. Domain specialists validated the accuracy and correctness of the obtained results.

Fudge: Fuzzy ontology building with consensuated fuzzy datatypes

An important problem in Fuzzy OWL 2 ontology building is the definition of fuzzy membership functions for real-valued fuzzy sets (so-called fuzzy datatypes in Fuzzy OWL 2 terminology). In this paper, we present a tool, called Fudge, whose aim is to support the consensual creation of fuzzy datatypes by aggregating the specifications given by a group of experts. Fudge is freeware and currently supports several linguistic aggregation strategies, including the convex combination, linguistic OWA, weighted mean and fuzzy OWA, and easily allows to build others in. We also propose and have implemented two novel linguistic aggregation operators, based on a left recursive form of the convex combination and of the linguistic OWA.

Storing fuzzy description logic ontology knowledge bases in fuzzy relational databases

In the context of the Semantic Web, fuzzy extensions to OWL (the W3C standard ontology language) and Description Logics (DLs, the logical foundation of OWL) have been extensively investigated, and there are many real fuzzy DL ontology knowledge bases. Therefore, how to store fuzzy DL ontology knowledge bases has become an important issue. In this paper, we propose an approach and implement a tool for storing fuzzy DL ontology knowledge bases in fuzzy relational databases. Our chosen formalism is a fuzzy extension of the very expressive DL SHOIN(D), which is the main logical foundation of the standard ontology language OWL, so that our storage approach can store not only fuzzy DL-knowledge bases but also fuzzy ontology knowledge bases. Firstly, we give a formal definition of fuzzy DL-knowledge bases. In the definition, we consider the constructors of both fuzzy SHOIN(D) DL and fuzzy OWL ontology and add some common fuzzy datatypes (e.g., trapezoidal values, interval values, approximate values, and labels) into the knowledge bases. On this basis, we propose an approach which can store fuzzy DL-knowledge bases in fuzzy relational databases, and provide an example to well explain the approach. The correctness and quality of the storage approach are proved and analyzed. Furthermore, following the proposed approach, we implemented a prototype tool, which can automatically store fuzzy DL-knowledge bases. Finally, we make a discussion about the query problem and make a comparison with the existing works.

Generalizing type-2 fuzzy ontologies and type-2 fuzzy description logics

In the last years, we are witnessing an increase of real-world applications of fuzzy ontologies. Most fuzzy ontologies are based on type-1 fuzzy logic, and type-2 fuzzy ontologies have not yet received such attention so far. Furthermore, there exists an important gap between type-2 knowledge representation formalisms (type-2 Description Logics) and type-2 fuzzy ontology applications. In this paper, we propose a formal framework for type-2 fuzzy ontologies taking into account the needs of existing applications. Essentially, our approach makes it possible to manage some uncertainty in the fuzzy membership functions used in the fuzzy datatypes and in the degrees of truth of the axioms. We define a type-2 Description Logic, a reasoning algorithm, and give a Fuzzy OWL 2 specification of it.

A fuzzy ontology modeling for case base knowledge in diabetes mellitus domain

Knowledge-Intensive Case-Based Reasoning Systems (KI-CBR) mainly depend on ontologies. Ontology can play the role of case-base knowledge. The combination of ontology and fuzzy logic reasoning is critical in the medical domain. Case-base representation based on fuzzy ontology is expected to enhance the semantic and storage of CBR knowledge-base. This paper provides an advancement to the research of diabetes diagnosis CBR by proposing a novel case-base fuzzy OWL2 ontology (CBRDiabOnto). This ontology can be considered as the first fuzzy case-base ontology in the medical domain. It is based on a case-base fuzzy Extended Entity Relation (EER) data model. It contains 63 (fuzzy) classes, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, and 105 fuzzy datatypes. We populated the ontology with 60 cases and used SPARQL-DL for its query. The evaluation of CBRDiabOnto shows that it is accurate, consistent, and cover terminologies and logic of diabetes mellitus diagnosis.

Fuzzy GML Modeling Based on Vague Soft Sets

The Open Geospatial Consortium (OGC) Geography Markup Language (GML) explicitly represents geographical spatial knowledge in text mode. All kinds of fuzzy problems will inevitably be encountered in spatial knowledge expression. Especially for those expressions in text mode, this fuzziness will be broader. Describing and representing fuzziness in GML seems necessary. Three kinds of fuzziness in GML can be found: element fuzziness, chain fuzziness, and attribute fuzziness. Both element fuzziness and chain fuzziness belong to the reflection of the fuzziness between GML elements and, then, the representation of chain fuzziness can be replaced by the representation of element fuzziness in GML. On the basis of vague soft set theory, two kinds of modeling, vague soft set GML Document Type Definition (DTD) modeling and vague soft set GML schema modeling, are proposed for fuzzy modeling in GML DTD and GML schema, respectively. Five elements or pairs, associated with vague soft sets, are introduced. Then, the DTDs and the schemas of the five elements are correspondingly designed and presented according to their different chains and different fuzzy data types. While the introduction of the five elements or pairs is the basis of vague soft set GML modeling, the corresponding DTD and schema modifications are key for implementation of modeling. The establishment of vague soft set GML enables GML to represent fuzziness and solves the problem of lack of fuzzy information expression in GML.

Modelling, representation and implementation of imperfect information for an enhanced exploration of large databases

The rapid development of social networks, intelligent sensors, mobile solutions and Internet of things has led to the emergence of large data-sets. Efficiently and effectively exploring these data-sets is a challenging question, especially when the imperfectness of real-world needs to be taken into account. The objective of this paper is thus to propose several solutions for modelling, representing and implementing imperfect information within large fuzzy databases. More specifically, in this paper imperfect information is modelled through a series of generic fuzzy data types, uniformly represented by means of possibility distributions and implemented using the basic constructs of object databases. These solutions are particularly useful for exploring large fuzzy databases since they permit to minimise the space required to store imperfect information, and access efficiently and effectively these databases.

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis.

Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems.

A fuzzy description logic F-SHIQ(G)

It is widely recognized that management of data types is an indispensable component in intelligent knowledge-based applications. Recent research has shown that OWL (Web Ontology Language) has a serious limitation on data types and does not support customized data types and predicates. Some work has been carried out to address this issue in crisp situations. In order to deal with fuzzy information in the Semantic Web, this paper proposes a new kind of fuzzy description logic F-SHIQ(G), which can support not only the representation and reasoning of fuzzy concept knowledge, but also fuzzy data information with customized fuzzy data types and customized fuzzy data type predicates. The syntax, semantics and reasoning algorithm of the F-SHIQ(G) are investigated in the paper, and its decidability, soundness, completeness and complexity of the reasoning algorithm are discussed in the paper also. For the reasoning for F-SHIQ(G), the paper adopts a traditional reasoning architecture for fuzzy data type reasoning and as a result, new fuzzy data type reasoners can be added without changing the fuzzy concept reasoner.

Comparison of entity with fuzzy data types in fuzzy object-oriented databases

Aiming at distinguishing entities, the concept of entity comparison plays an important role in object management in object-oriented databases as well as object-oriented knowledge bases. In order to model uncertain data and complex-valued attributes a

On Modeling the Behavior of Comparators for Complex Fuzzy Objects in a Fuzzy Object-Relational Database Management System

Abstract This paper proposes a parameterized definition for fuzzy comparators on complex fuzzy datatypes like fuzzy collections with conjunctive semantics and fuzzy objects. This definition and its implementation on a Fuzzy Object-Relational Database Management System (FORDBMS) provides the designer with a powerful tool to adapt the behavior of these operators to the semantics of the considered application.

Fuzzy Arden Syntax: A fuzzy programming language for medicine

The programming language Arden Syntax has been optimised for use in clinical decision support systems. We describe an extension of this language named Fuzzy Arden Syntax, whose original version was introduced in S. Tiffe's dissertation on "Fuzzy Arden Syntax: Representation and Interpretation of Vague Medical Knowledge by Fuzzified Arden Syntax" (Vienna University of Technology, 2003). The primary aim is to provide an easy means of processing vague or uncertain data, which frequently appears in medicine. For both propositional and number data types, fuzzy equivalents have been added to Arden Syntax. The Boolean data type was generalised to represent any truth degree between the two extremes 0 (falsity) and 1 (truth); fuzzy data types were introduced to represent fuzzy sets. The operations on truth values and real numbers were generalised accordingly. As the conditions to decide whether a certain programme unit is executed or not may be indeterminate, a Fuzzy Arden Syntax programme may split. The data in the different branches may be optionally aggregated subsequently. Fuzzy Arden Syntax offers the possibility to formulate conveniently Medical Logic Modules (MLMs) based on the principle of a continuously graded applicability of statements. Furthermore, ad hoc decisions about sharp value boundaries can be avoided. As an illustrative example shows, an MLM making use of the features of Fuzzy Arden Syntax is not significantly more complex than its Arden Syntax equivalent; in the ideal case, a programme handling crisp data remains practically unchanged when compared to its fuzzified version. In the latter case, the output data, which can be a set of weighted alternatives, typically depends continuously from the input data. In typical applications an Arden Syntax MLM can produce a different output after only slight changes of the input; discontinuities are in fact unavoidable when the input varies continuously but the output is taken from a discrete set of possibilities. This inconvenience can, however, be attenuated by means of certain mechanisms on which the programme flow under Fuzzy Arden Syntax is based. To write a programme making use of these possibilities is not significantly more difficult than to write a programme according to the usual practice.

An expert system approach for the choice of appropriate data types in a Fuzzy Database

Several applications need to stock and interrogate fuzzy data in a flexible manner. Unfortunately, these two requirements are not supported by the database management systems which currently dominate the databases servers market. Several works have tried to cure these insufficiencies; we speak henceforth of fuzzy databases and flexible querying of these databases. The design problem of such databases did not receive the interest that it deserves. The fuzzy data types are numerous and varied what puts the designer of a fuzzy database in front of a complex problem of choice. In this paper, we propose an approach based on an expert system which helps the designer to choose the appropriate data types for the columns susceptible to stock fuzzy data or interrogated by flexible queries. This solution suits the migration toward a fuzzy database as well as a fuzzy database under design. Although this solution is based on the fuzzy model GEFRED (GEneralised model for Fuzzy RElational Database), it can be applied to other fuzzy models. This expert system can be integrated into the classic design environments like Rational Rose, Power AMC, etc.

Representing and processing fuzzy information — an XML-based approach

Fuzzy approaches to improve decision making within business applications are widely spread. However, these solutions are often restricted to special areas, and are less than fully integrated into company-wide business application systems. We propose to use a common way to represent fuzzy information to soften this lack of integration. In this paper, we introduce a formal syntax for important fuzzy data types used to store fuzzy information. We make this syntax operatable by defining appropriate document type definitions (DTD), and we show how fuzzy information, whose description is based on these DTDs, can be exchanged between application systems by means of the extensible markup language (XML). With this, we give an approach to a better integration of business applications using fuzzy approaches into company-wide business application systems. This allows for a better collaboration of application systems and development tools, which use fuzzy approaches or which are intended to develop fuzzy approaches, in general.

The Fractal Data or is Fuzzy Structuration Meaningful?

After briefly recalling the definitions of our mathematical model of structuration : the R-Structures and the principles of another model of structuration the Hypergraph Based Data Structures, which allows the manipulation of fuzzy information at the level of data capture, we explain how they can be drawn closer to each other. Our algorithms of correspondence which allow to turn any H.B. Data Structure into a R-Structure are associated with the H.B. Data Structuration of the R-Structures to show that the R-Structures are a powerful tool for the H.B.D.S. manipulation. We use the Fuzzy Set Theory to consider fuzzy R-Structures. Taking as a basis the fuzzy Abstract Data Types of the H.B.D.S. we show our results include them and lead to suggest new types of fuzzy data of materialization. Then a quite new concept, fuzzy skeleton of structure, is discussed. It is shown their manipulation is made possible by the previous results. The most important point is the presentation of a type of fuzziness between materialization level and skeleton level, we named the Fractal Data. To conclude we go further the scientific and theoritical meaning we have given to the fuzzy structuration to reach an epistemological meaning.