Afef Bahri

Dealing with Similarity Relations in Fuzzy Ontologies

Crisp ontologies become less suitable in all domains in which the concepts to be represented have imprecise definitions. Fuzzy ontologies are developed to overcome this problem. Determining similarity relations among fuzzy ontology components is essential for many reuse subprocesses, for query rewriting as well as for fuzzy ontology modeling. Denning similarity relations within fuzzy context may be realized basing on the linguistic similarity among ontology components or may be deduced from their intensional definitions. The later approach needs to be dealt with differently in crisp and fuzzy ontologies. In the first case, two ontology components are related by a given similarity relation or not. In the second one, two ontology components have a degree to be related by a given similarity relation. This is the scope of this paper.

Fuzzy ontologies building method: Fuzzy OntoMethodology

Building ontologies is very important for diverse domains and especially for semantic Web. We find in the literature many methods and tools for this building. However, the fuzzy aspect is not enough studied in these methods and tools, whereas information systems can include uncertainties and imperfections. The goal of the definition of fuzzy ontologies is to integrate these characteristics. So, we must be able to modulate uncertainties, on the one hand, and to product representations accessible and understandable by machines, on the other hand. If we find actually many building methods and editors for classic ontologies (i.e., crisp or exact), we do not find such methods for fuzzy ontologies. Then, this paper defines our work for fuzzy ontologies building. It presents our fuzzy ontologies building method “Fuzzy OntoMethodology”.

Querying fuzzy RDFS semantic annotations

The Semantic Web is an infrastructure that enables the interchange, the integration and the reasoning about information on the Web. In the Semantic Web, the resources are described using particular metadata called “Semantic annotations”. A semantic annotation is a particular case of annotation which refers to ontology. The Web content is, for the most part, subject to uncertainty or imperfection. If many extensions of ontology languages have been proposed to deal with fuzzyness on the Semantic Web, the problem of fuzzy ontology querying is not well treated. We propose in this paper a fuzzy extension of the RDFS model. We propose a new query language named FSAQL to query fuzzy RDFS semantic annotations. We define the syntax and the semantics of this language and the way to evaluate it.

Fuzzy ontology implementation and query answering on databases

The conceptual formalisms supported by typical ontologies may be not sufficient for handling imprecise information commonly found in many application domains. Fuzzy ontologies are developed to overcome this problem. The implementation, inference and query of fuzzy ontologies gain more attention in recent years. This paper addresses fuzzy ontology implementation and query answering on databases. We propose a language to define fuzzy ontology schema and to query fuzzy ontology databases and an inferential engine to infer fuzzy concept instances and their membership degrees.

A Tree-based Algorithm for Ranking Web Services

The aim of this paper is to propose a new algorithm for Web services ranking. The proposed algorithm relies on a tree data structure that is constructed based on the scores of Web services. Two types of scores are considered, which are computed by respectively selecting the edge with the minimum or the edge with the maximum weight in the matching graph. The construction of the tree requires the successive use of both scores, leading to two different versions of the tree. The final ranking is obtained by applying a pre-order traversal on the tree and picks out all leaf nodes ordered from the left to the right. The performance evaluation shows that the proposed algorithm is most often better than similar ones.

Datalog-based framework for efficient query answering over fuzzy ontologies

Ontologies are set to play a key role in many domains, especially in the context of the Semantic Web. Ontology languages are based on crisp logic and thus cannot handle imprecision. Fuzzy extensions of ontology languages become unuseful if they cannot be processed by applications. In fact, despite their inference capabilities over complex TBoxes, description logic reasoners have a high ABox reasoning complexity. That is, to retrieve the instances of a given concept, we need to run the reasoning algorithm for each individual in the ABox. In Datalog systems, query answers are computed in one pass: bottom-up or top-down. Motivated by this concern, we propose in this paper a Datalog based framework, named OntoFlex, for efficient query answering over fuzzy ontologies. The framework implements the FSAQL query language over Flora-2 and XSB Datalog systems and proposes a correct mapping of fuzzy RDFS and fuzzy OWL2EL into crisp Datalog programs.

A generalized fuzzy extension of EL

Fuzzy extensions of Description Logics are generally based on precise membership functions which assign a particular real number to an element or a subset of the universe. In some cases where we may only be able to identify approximately appropriate membership degrees the use of precise membership functions become unsuitable. In this paper we propose a generalized fuzzy extension of the description logic EL⁺⁺, named Gf-EL⁺⁺, based on interval-valued fuzzy sets. We present the syntax and the semantics of Gf-EL⁺⁺ which uses an extension of concept subsumption with “interval-valued fuzzy subsumption”. We equally propose a tractable subsumption algorithm for Gf-EL⁺⁺.

A framework for parameterized semantic matchmaking and ranking of web services

The Parameterized Semantic Matchmaking and Ranking (PMRF) is a highly configurable framework supporting a parameterized matching and ranking of Web services. The paper introduces the matching and ranking algorithms supported by the PMRF and presents its architecture. It also evaluates the performance of the PMRF and compares it to the iSeM-logic-based and SPARQLent frameworks using the OWLS-TC4 datasets. The comparison results show that the algorithms included in PMRF behave globally well in comparison to iSeM-logic-based and SPARQLent.

Enhanced Fuzzy Object-Relational database Model for efficient implementation of the FSM

The objective of this paper is to present the first results concerning the mapping and implementation of the Fuzzy Semantic Model (FSM) as a Fuzzy Object-Relational database Model (FuzzORM). This solution permits to take advantages of both relational and object-oriented databases. The object-relational database management system PostgreSQL has been used for the implementation of the FuzzORM.

Efficient versus accurate algorithms for computing a semantic logic-based similarity measure

There are three types of Web services matchmakers: logic-based, non logic-based, and hybrid. Logic-based matchmakers employ the semantics of the Web services. Non-logic based matchmakers employ other approaches such as syntactic and structural matching. Hybrid matchmakers combine both approaches. This paper presents and compares two algorithms for computing a semantic logic-based similarity measure in the perspective of Web service matching. The first algorithm offers an efficient solution, while the second algorithm proposes a more accurate result. Both algorithms are evaluated using the SME2 tool. Performance evaluation shows that efficient algorithm reduces substantially the computing time while the accurate algorithm ameliorates considerably the precision of the matching process.