An Hybrid Architecture Integrating Forward Rules with Fuzzy Ontological Reasoning

Bragaglia, Stefano; Chesani, Federico; Ciampolini, Anna; Mello, Paola; Montali, Marco; Sottara, Davide

doi:10.1007/978-3-642-13769-3_53

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…Bragaglia et al [18] propose a hybrid architecture combining forward rules and fuzzy ontology reasoning. Although the set of fuzzy inferences covers both fuzzy ontological and fuzzy rule reasoning, their proposal does not exploit Mamdani reasoning neither defuzzification methods.…”

Section: Related Workmentioning

confidence: 99%

A FML-based hybrid reasoner combining fuzzy ontology and Mamdani inference

Yaguinuma

Santos

Camargo

et al. 2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Fuzzy ontologies have been employed to represent and reason over fuzzy information, which often occurs in real-world applications. Fuzzy inference systems (FIS) are well-known computational intelligence systems whose inferences can also be exploited in fuzzy ontology-based applications. Specifically, the combination of fuzzy ontologies and Mamdani-type FIS can provide inferences involving fuzzy rules and numerical property values, which can be considered in other fuzzy ontology reasoning tasks. In this sense, this paper proposes a hybrid reasoner combining fuzzy ontology and Mamdani inference to provide meaningful inferences that are not available to fuzzy ontologybased applications in an integrated way. Fuzzy rules are represented with Fuzzy Markup Language, providing an abstraction level with regard to the underlying FIS implementation. Some experiments are presented regarding a recommender system context, including a comparison with a fuzzy description logic reasoner in terms of fuzzy rule reasoning semantics and integration issues.

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Section: Related Workmentioning

confidence: 99%

A FML-based hybrid reasoner combining fuzzy ontology and Mamdani inference

Yaguinuma

Santos

Camargo

et al. 2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Fuzzy ontology mapping (Niwattanakul et al, 2007;Buche et al, 2008;Ferrara et al, 2008;Fernández et al, 2009;Xu et al, 2010;Bakillah & Mostafavi, 2011;Todorov et al, 2014) Determining similarity relations among fuzzy ontologies for fuzzy ontology mapping (Bahri et al, 2005(Bahri et al, , 2007Calì et al, 2007;Castano et al, 2008;Cao et al, 2009;Cai & Leung, 2011) Fuzzy ontology integration Nguyen & Truong, 2010;Duong et al, 2011;Truong et al, 2011;Truong & Quach, 2014) Fuzzy ontology evaluation (Parry, 2006b;Ivanova, 2008;Asma & Zizette, 2014) Fuzzy OWL 2 ontology (Bobillo, 2008;Bobillo & Straccia, 2009a) Combining fuzzy ontology with other knowledge representation formalisms (Lukasiewicz, 2006;Straccia, 2004bStraccia, , 2006aStraccia, , 2006bLukasiewicz & Straccia, 2007;Venetis et al, 2007;Bragaglia et al, 2010;Liu et al, 2013) …”

Section: Mapping and Integration Evaluation Extensionmentioning

confidence: 99%

“…Bragaglia et al (2010) combined two technologies such as fuzzy ontology and rule to provide a unified framework for supporting fuzzy reasoning. Bragaglia et al (2010) combined two technologies such as fuzzy ontology and rule to provide a unified framework for supporting fuzzy reasoning.…”

Section: Extension Of Fuzzy Ontologiesmentioning

confidence: 99%

“…Baziz et al . (2006) proposed an approach to information retrieval based on the use of a fuzzy conceptual structure (ontology) that is used both for indexing document and expressing user queries. Calegari and Sanchez (2008) showed how a fuzzy ontology-based approach can improve semantic documents retrieval.…”

Section: Applications and Other Issues Of Fuzzy Ontologiesmentioning

confidence: 99%

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A survey on fuzzy ontologies for the Semantic Web

Zhang¹,

Cheng²,

Ma³

2016

The Knowledge Engineering Review

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Ontology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real-world applications. Thus, a requirement of extending ontologies naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web. In order to provide the necessary means to handle such vague and imprecise information there are today many proposals for fuzzy extensions to ontologies, and until now the literature on fuzzy ontologies has been flourishing. To investigate fuzzy ontologies and more importantly serve as helping readers grasp the main ideas and results of fuzzy ontologies, and to highlight an ongoing research on fuzzy approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains, in this paper, we provide a comprehensive overview of fuzzy ontologies. In detail, we first introduce fuzzy ontologies from the most common aspects such as representation (including categories, formal definitions, representation languages, and tools of fuzzy ontologies), reasoning (including reasoning techniques and reasoners), and applications (the most relevant applications about fuzzy ontologies). Then, the other important issues on fuzzy ontologies, such as construction, mapping, integration, query, storage, evaluation, extension, and directions for future research, are also discussed in detail. Also, we make some comparisons and analyses in our whole review.ontology definition languages, such as RDF(S), SHOE, OIL, DAML, DAML + OIL, and web ontology language (OWL), have been developed over the past years (Horrocks et al., 2003). The OWL (Smith et al., 2004) and its successor OWL 2 (Cuenca Grau et al., 2008) is the W3C recommended ontology representation language. The theoretical underpinnings of OWL and ontologies are strongly based on Description Logics (DLs), a subset of first-order logic especially suitable for representing structured knowledge (Horrocks & Sattler, 2001;Baader et al., 2003;Horrocks et al., 2003).Although ontology is a quite expressive formalism, it features limitations, mainly with what can be said about fuzzy information. Nowadays, in ontology-based and many applications information is often vague and imprecise. This is a well-known problem especially for semantics-based applications of the Semantic Web, such as knowledge management, e-commerce, and web portals. For example, a task like a 'doctor appointment' could look like: 'Make me an appointment with a doctor close to my home not too early and of good references' (Stoilos et al., 2010). The conceptual formalism supported by typical ontology may not be sufficient to represent such information and knowledge. Therefore, many proposals have attempted to apply different formalisms...

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“…Thus, their set of fuzzy inferences does not include fuzzy concept knowledge reasoning (question 4), provided by fuzzy ontology reasoners. (Bragaglia et al, 2010) propose a hybrid architecture combining forward rules and fuzzy ontology reasoning. Although the set of fuzzy inferences covers both fuzzy ontological and fuzzy rule reasoning, their proposal does not exploit Mamdani reasoning neither defuzzification methods.…”

Section: Related Workmentioning

confidence: 99%

HyFOM Reasoner: Hybrid Integration of Fuzzy Ontology and Mamdani Reasoning

2013

Proceedings of the 15th International Conference on Enterprise Information Systems

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Some real-world applications require representation and reasoning regarding imprecise or vague information. In this context, the appropriate combination of fuzzy ontologies and Mamdani fuzzy inference systems can provide meaningful inferences involving fuzzy rules and numerical property values. In general, this knowledge is not obtained through typical fuzzy ontology reasoning and can be relevant for some ontology reasoning tasks that depend on numerical property values. To address this issue, this paper proposes the HyFOM reasoner, which provides a hybrid integration of fuzzy ontology and Mamdani reasoning. A real-world case study involving the domain of food safety is presented, including comparative results with a state-of-the-art fuzzy description logic reasoner.

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An Hybrid Architecture Integrating Forward Rules with Fuzzy Ontological Reasoning

Cited by 16 publications

References 8 publications

A FML-based hybrid reasoner combining fuzzy ontology and Mamdani inference

A FML-based hybrid reasoner combining fuzzy ontology and Mamdani inference

A survey on fuzzy ontologies for the Semantic Web

HyFOM Reasoner: Hybrid Integration of Fuzzy Ontology and Mamdani Reasoning

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