Fuzzy OWL Research Articles

FUKG: answering flexible queries over knowledge graphs

PurposeThere is an increasing interest in the use of knowledge graphs to represent real-world knowledge and a common need to manage imprecise knowledge in many real-world applications. This paper aims to study approaches to solve flexible queries over knowledge graphs.Design/methodology/approachBy introducing fuzzy logic in the query answering process, the authors are able to obtain a novel algorithm to solve flexible queries over knowledge graphs. This approach is implemented in the FUzzy Knowledge Graphs system, a software tool with an intuitive user-graphical interface.FindingsThis approach makes it possible to reuse semantic web standards (RDF, SPARQL and OWL 2) and builds a fuzzy layer on top of them. The application to a use case shows that the system can aggregate information in different ways by selecting different fusion operators and adapting to different user needs.Originality/valueThis approach is more general than similar previous works in the literature and provides a specific way to represent the flexible restrictions (using fuzzy OWL 2 datatypes).

Integration of Fuzzy Ontologies and Neural Networks in the Detection of Time Series Anomalies

This paper explores an approach to solving the problem of detecting time series anomalies, taking into account the specifics of the subject area. We propose a method based on the integration of a neural network with long short-term memory (LSTM) and Fuzzy OWL (Fuzzy Web Ontology Language) ontology. A LSTM network is used for the mathematical search for anomalies in the first stage. The fuzzy ontology filters the detection results and draws an inference for decision making in the second stage. The ontology contains a formalized representation of objects in the subject area and inference rules that select only those anomaly values that correspond to this subject area. In the article, we propose the architecture of a software system that implements this approach. Computational experiments were carried out on free data of technical characteristics of drilling rigs. The experiments showed high efficiency, but not the maximum efficiency of the proposed approach. In the future, we plan to select a more efficient neural network architecture for mathematical anomaly detection. We also plan to develop an algorithm for automatically filling the rules of inference into the ontology when analyzing text sources.

Fuzzy OWL-Boost: Learning fuzzy concept inclusions via real-valued boosting

OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given an OWL ontology and a target class T, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T (and to which degree). To do so, we present Fuzzy OWL-Boost that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation with several ontologies.

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.

A fuzzy ontology-based approach for tool-supported decision making in architectural design

In software development, non-functional requirements (NFRs) play a crucial role in decision-making procedures for architectural solutions. A strong relation exists between NFRs and design patterns, a powerful method to support the architectural design of software systems, but due to their complexity and abstraction, NFRs are rarely taken into account in software design. In fact, the knowledge on NFRs is usually owned by designers and not formalized in a structured way. We propose to structure the knowledge associated with NFRs via a Fuzzy Ontology, which we show is able to model their mutual relations and interactions. The declarative approach makes possible to represent and maintain the above-mentioned knowledge by keeping the flexibility and fuzziness of modeling thanks to the use of fuzzy concepts such as high, low, fair. We present a decision support system based on (i) a fuzzy OWL 2 ontology that encodes 109 design patterns, 28 pattern families and 37 NFRs and their mutual relations, (ii) a novel reasoning service to retrieve a ranked list of pattern sets able to satisfy the non-functional requirements within a system specification.

Reasoning within Fuzzy OWL 2 EL revisited

Description Logics (DLs) are logics with interesting representational and computational features and are at the core of the Web Ontology Language OWL 2 and its profiles among which there is OWL 2 EL. The main feature of OWL 2 EL is that instance/subsumption checking can be decided in polynomial time. On the other hand, fuzzy DLs have been proposed as an extension to classical DLs with the aim of dealing with fuzzy concepts and we focus here on Fuzzy OWL 2 EL under standard and Gödel semantics. We provide some reasoning algorithms showing that instance/subsumption checking decision problems remain polynomial time for Fuzzy OWL 2 EL. We also identify some issues in previous related work (essentially incompleteness problems).

Reverse engineering of Fuzzy OWL 2 ontologies to object-oriented database models

With the increase in demand of complex information modeling, object-oriented database models are put on the agenda. But information imperfection is inherent in the real-world applications. To deal with these complex imprecise and uncertain information, fuzzy object-oriented database (FOOD) and fuzzy OWL 2 ontology modeling are recently received more attention. But construction fuzzy ontology is a time-consuming and laborious task from scratch, reusing existing fuzzy ontology is an effective method of ontology construction. For the sake of reusing fuzzy OWL 2 ontologies, this paper proposes a reverse engineering approach for transforming fuzzyOWL2 Ontologies into FOOD models. And reverse engineering can shorten development cycles of ontology and various database models. On this basis, we propose formal definition of FOOD models and fuzzy OWL 2 ontologies. Furthermore, we give transformation rules and explain how to transform fuzzy OWL 2 ontologies into fuzzy FOOD models with an example in detail. The correctness of this transformation approach is proved. The advantage of reengineering fuzzy ontologies into FOOD models is the reusability of domain knowledge on the Web.

A Formal Approach of Construction Fuzzy XML Data Model Based on OWL 2 Ontologies

With the rapid development of the Web, a large number of electronic resources have been generated. Currently, XML has been an important tool for data representation and exchange over the Web. The incompleteness of information in the real-world is inherent. To deal with imprecise and uncertain data, fuzzy XML and fuzzy ontology modeling recently receive more attention. In order to represent the fuzzy information, we concentrate on fuzzy information modeling in a fuzzy XML model and fuzzy OWL 2 ontology in this paper. Furthermore, we propose an approach of transforming fuzzy OWL 2 ontologies (including structures and instances) into fuzzy XML models. Then we prove that the sementics of this transformation approach are preserved and propose a transforming example to explain the transforming process. This paper provides a new approach for the fuzzy XML modeling and fuzzy XML mapping based on the fuzzy OWL 2 ontologies.

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.

Developing a fuzzy OWL ontology for obesity related cancer domain

Developing a fuzzy OWL ontology for obesity related cancer domain

Developing a fuzzy OWL ontology for obesity related cancer domain

Obesity is associated with various diseases, particularly cardiovascular diseases, diabetes type 2, obstructive sleep apnea, certain types of cancer, osteoarthritis, and asthma. The knowledge of the obesity related cancer (ORC) domain is highly required to be represented in a structured and formalised shape. In this paper, we develop an ontology to represent ORC domain knowledge with its diseases, symptoms, diagnosis, and treatments. The proposed ontology is based on the Web Ontology Language (OWL 2) integrated with the fuzzy logic. The fuzzy developed ontology handles the overlapping concepts, ingesting more concepts, and copes with the linguistic domain variables, which were not possible using the regular ontologies. It allows the users to query the fuzzy Dl reasoner for element and answer them with the fuzzy ontology. By developing the fuzzy ORC ontology, it is expected to be a good practice for the ontologists and knowledge engineers.

The Role of Crisp Elements in Fuzzy Ontologies: The Case of Fuzzy OWL 2 EL

In the last years, fuzzy ontologies and fuzzy description logics (DLs) have been applied successfully to several real-world domains. Some recent results on fuzzy DLs show an increase in the theoretical complexity, even for not expressive languages. However, it still may be possible to obtain reasoning algorithms performing well in practice. This paper studies some cases where reasoning with a fuzzy ontology can be reduced to reasoning with a tractable classical language (an OWL 2 profile). Then, building on the assumption that not all concepts and roles in fuzzy ontologies are indeed fuzzy, we show that reasoning algorithms can be optimized by taking into account the existence of possible crisp concepts and roles. We also prove the usefulness on crisp representation reasoning algorithms over an empirical evaluation over fuzzy OWL 2 EL ontologies, and discuss some preliminary techniques to learn which elements should be considered to be crisp or noncrisp.

Chest-Pain Consultation System Based on Fuzzy Semantic Rules

<p>Chest pain is considered to be one of the most important prognoses to diagnose several heart and chest diseases. This domain of knowledge suffers from vague information especially when non specialist people interact with it. This paper presents a consultation system to be used by patients with chest pain or discomfort in order to diagnose the cause of the pain and provide suitable recommendations. The system consists of two subsystems. Determining the caused of the pain is the first stage based on the fuzzy concepts and fuzzy OWL to deal with vague information. Depending on the result of the first stage, the second stage provides recommendations to be accomplished by the patient to save their lives according to SWRL and Ontology engineering. The system has been tested using 55 patients to validate its sensitivity and specificity. The results were compared with the specialists’ opinion and the results emphasized the efficiency and the reliability of the system.</p>

A Framework for Fuzzy Ontology Storing onto Relational Database within an a Priori Data Integration System

in data integration systems; but it can be strongly fixed when using ontology in an a priori approach where local ontology concepts are linked with shared ontology prior to populating data in corresponding sources. In this paper, we describe a defying context where local source is described by a fuzzy OWL ontology within an integration system using an a priori approach to achieve automatic integration for new data sources. We propose a conceptual framework starting by shared ontology and producing a target fuzzy Relational Database for every ontology-based local source participating in the integration system. Assuming shared ontology is a consensus in a given domain, this framework provides various contributions. It aims to solve ahead the problem of heterogeneous data sources because the local ontology that references the shared ontology is used to generate the conceptual data model for the target fuzzy Relational Database. To do this, it extends the a priori approach to deal with uncertainty which is a very common requirement in real world applications. Its storage process may be run on most of popular RDBMS. It is using a fuzzy OWL which represents most of fuzzy ontology constructs.

A Fuzzy Extension to the OWL 2 RL Ontology Language

Fuzzy extensions to description logics (DLs) have gained considerable attention the last decade. So far most works on fuzzy DLs have focused on either very expressive languages, like fuzzy OWL and OWL 2, or on highly inexpressive ones, like fuzzy OWL 2 QL and fuzzy OWL 2 EL. To the best of our knowledge, a fuzzy extension to the language OWL 2 RL has not been thoroughly studied so far. This language is very relevant since it combines both adequate expressive power as well as efficient reasoning algorithms which can be realized using rule-based (Datalog) technologies. In contrast to previous fuzzy extensions, a fuzzy extension of OWL 2 RL is not a straightforward task for the following reason. The main motivation of OWL 2 RL is that its axioms can be equivalently represented as Datalog rules. Hence, to achieve our goal we need to investigate which OWL 2 RL axioms when interpreted under the fuzzy setting can be transformed to equivalent fuzzy Datalog rules. We show that this is not, in general, possible for all axioms but we show that this ‘issue’ can to a large extent be alleviated. Moreover, we have performed an experimental evaluation with many well-known ontologies which showed that such axioms are not used so often in practice.

A fuzzy ontology for semantic modelling and recognition of human behaviour

We propose a fuzzy ontology for human activity representation, which allows us to model and reason about vague, incomplete, and uncertain knowledge. Some relevant subdomains found to be missing in previous proposed ontologies for this domain were modelled as well. The resulting fuzzy OWL 2 ontology is able to model uncertain knowledge and represent temporal relationships between activities using an underlying fuzzy state machine representation. We provide a proof of concept of the approach in work scenarios such as the office domain, and also make experiments to emphasize the benefits of our approach with respect to crisp ontologies. As a result, we demonstrate that the inclusion of fuzzy concepts and relations in the ontology provide benefits during the recognition process with respect to crisp approaches.

Aggregation operators for fuzzy ontologies

Fuzzy ontologies extend classical ontologies to allow the representation of imprecise and vague knowledge. Although a relatively important amount of work has been carried out in the field during the last years and they have been successfully used in several applications, several notions from fuzzy logic have not been considered yet in fuzzy ontologies. Among them are aggregation operators, mathematical functions used to fuse different pieces of information, which is a very common need. Some examples of aggregation operators are weighted sums, Ordered Weighting Averaging operators and fuzzy integrals.In this work, we integrate fuzzy ontologies and aggregation operators. As a theoretical formalism, we provide the syntax and semantics of a fuzzy Description Logic with fuzzy aggregation operators. We provide a reasoning algorithm for the family of operators that are representable using a Mixed Integer Linear Programming optimization problem. We also show how to encode some examples of aggregation operators using the language Fuzzy OWL 2.

Construction of fuzzy OWL ontologies from fuzzy EER models: A semantics-preserving approach

Currently, many research studies have been concentrated on construction of fuzzy ontologies from different sources. Over the years, standard and variations of the entity-relationship (ER) model has widespread use, and also some approaches have been proposed for modeling fuzzy information in ER and extended entity-relationship (EER) models. Therefore,constructing fuzzy ontologies from fuzzy EER models may be valuable to the development of fuzzy ontologies, and in turn the constructed fuzzy ontologies may be useful for reasoning about the fuzzy EER models. In this paper, we develop a semantics-preserving approach and an automated tool for constructing fuzzy ontologies from fuzzy EER models, and investigate how to apply the constructed fuzzy ontologies to reason on fuzzy EER models. The constructed fuzzy ontologies are represented by the fuzzy OWL language, which is a fuzzy extension of the W3C recommendation standard ontology language OWL (Web Ontology Language). Firstly, we give the formalization of fuzzy EER models. On this basis, we propose an approach for constructing fuzzy OWL ontologies from fuzzy EER models, which can transform fuzzy EER models (including the structure and instance information of fuzzy EER models) into fuzzy OWL ontologies. Also, we give the proof for the semantics preservation of the transformation, and provide a detailed transformation example. Furthermore, we implement a prototype tool FEER2FOnto, which can automatically construct fuzzy OWL ontologies from fuzzy EER models. Finally, we investigate how to reason on fuzzy EER models with the constructed fuzzy OWL ontologies, and propose the reasoning approach and provide a reasoning example.

Reasoning of fuzzy relational databases with fuzzy ontologies

A significant interest developed regarding the problem of describing databases with expressive knowledge representation techniques in recent years, so that database reasoning may be handled intelligently. Therefore, it is possible and meaningful to investigate how to reason on fuzzy relational databases (FRDBs) with fuzzy ontologies. In this paper, we first propose a formal approach and an automated tool for constructing fuzzy ontologies from FRDBs, and then we study how to reason on FRDBs with constructed fuzzy ontologies. First, we give their respective formal definitions of FRDBs and fuzzy Web Ontology Language (OWL) ontologies. On the basis of this, we propose a formal approach that can directly transform an FRDB (including its schema and data information) into a fuzzy OWL ontology (consisting of the fuzzy ontology structure and instance). Furthermore, following the proposed approach, we implement a prototype construction tool called FRDB2FOnto. Finally, based on the constructed fuzzy OWL ontologies, we investigate how to reason on FRDBs (e.g., consistency, satisfiability, subsumption, and redundancy) through the reasoning mechanism of fuzzy OWL ontologies, so that the reasoning of FRDBs may be done automatically by means of the existing fuzzy ontology reasoner.© 2012 Wiley Periodicals, Inc.