Designing a Decision Support System for Recommending Smartphones Using Fuzzy Ontologies

Morente-Molinera, Juan Antonio; Pérez, Ignacio Javier; Wikström, Robin; Viedma, Enrique Herrera; Carlsson, Christer

doi:10.1007/978-3-319-11310-4_28

Cited by 4 publications

(6 citation statements)

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“…In Loia (2011), how to join fuzzy ontologies and fuzzy markup languages was introduced, and the joint exploitation of these technologies will allow web intelligence designers to develop fuzzy inference mechanisms and semantic decision-making systems for an efficient modeling of real scenarios. In addition, Molinera et al (2014) designed a decision support system build over a fuzzy ontology in order to help people to select the perfect smartphone for them. To overcome these limitations, proposed a semantic fuzzy expert system which implements a generic framework for the balanced scorecard, where the knowledge about balanced scorecard variables is represented using an OWL ontology, and the ontology acts as the basis for the fuzzy expert system, which uses highly interpretable fuzzy IF-THEN rules to infer new knowledge.…”

Section: Decision Makingmentioning

confidence: 99%

“…RDF (the main Semantic Web data format) is a quite popular Semantic Web representation formalism. However, under the classical semantics, RDF cannot represent vague information and, to this purpose, fuzzy extension of RDF has been done as will be shown in the following: Fuzzy extension of RDF : to represent fuzzy data, Mazzieri (2004) and Mazzieri and Dragoni (2005, 2008) defined a syntactic and semantic extension of RDF. Following the same guidelines, how to extend from RDF Schema to fuzzy RDF Schema was also discussed, and also they described an implementation strategy that relies on translating the fuzzy triples into plain RDF triples by using reification.…”

Section: Representation Of Fuzzy Ontologiesmentioning

confidence: 99%

“…Fuzzy extension of RDF : to represent fuzzy data, Mazzieri (2004) and Mazzieri and Dragoni (2005, 2008) defined a syntactic and semantic extension of RDF. Following the same guidelines, how to extend from RDF Schema to fuzzy RDF Schema was also discussed, and also they described an implementation strategy that relies on translating the fuzzy triples into plain RDF triples by using reification.…”

Section: Representation Of Fuzzy Ontologiesmentioning

confidence: 99%

“…It also enhances the existing ontology with new concepts and their descriptors which may be precise and/or imprecise, mined from the text. Furthermore, the mined knowledge could be processed for clustering or integrating resources Decision making Decision making in business management Loia, 2011;Carlsson et al, 2012;Lisi & Straccia, 2013;Pérez et al, 2013;Molinera et al, 2014) The fuzzy ontologies allow web intelligence designers to develop fuzzy inference mechanisms and semantic decision-making systems for an efficient modeling of real scenarios Knowledge representation and reasoning Aerospace real options valuation (Rodger, 2013) Cascade multi-agent system (Hadjiski, 2008) Computer games (Ling et al, 2007) Computing with words (Reformat & Ly, 2009) Traffic transportation system (Zhai et al, 2007(Zhai et al, , 2008 Weather forecast (Truong et al, 2011) User profile modeling (Ferreira-Satler et al, 2014) Building information model (Gómez-Romero et al, 2015) Human activity recognition (Rodríguez et al 2014) Imprecise temporal/spatial knowledge representation (Nagypal & Motik, 2003;Hudelot et al, 2008;Parry, 2008;He et al, 2014) The fuzzy ontologies are often employed to represent and reason on the domain knowledge that results in enhancing the semantics, avoiding the conflicts and solving the problems of some special applications Table 9 The other issues of fuzzy ontologies…”

Section: Semantics Extraction and Analysismentioning

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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Section: Decision Makingmentioning

confidence: 99%

Section: Representation Of Fuzzy Ontologiesmentioning

confidence: 99%

Section: Representation Of Fuzzy Ontologiesmentioning

confidence: 99%

Section: Semantics Extraction and Analysismentioning

confidence: 99%

See 2 more Smart Citations

A survey on fuzzy ontologies for the Semantic Web

Zhang¹,

Cheng²,

Ma³

2016

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…Unfortunately, implementing F-AHP and F-ANP for the smartphone selection case has not yet been found. Molinera et al, tried designing a decision support system for recommending smartphones using fuzzy ontologies [20]. The design application used linguistic modeling to provide users with preferences on smartphone features in fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Decision Support System for Smartphone Recommendation: The Comparison of Fuzzy Ahp and Fuzzy Anp in Multi-Attribute Decision Making

Okfalisa

Rusnedy

Iswavigra

et al. 2020

Sinergi

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Previous researches outlined the advantages of the Analytical Hierarchy Process (AHP) and Analytic Network Process (ANP) methods in solving Multi-Attribute Decision Making (MADM) problems. The advancement of the above methods was continually developed as an effort to cover up various weaknesses. Mainly related to the consistency and linguistic variables in translating the expert opinions. Thus, it initialized the emergence of Fuzzy AHP (F-AHP) and Fuzzy ANP (F-ANP). Due to the restricted operation of these algorithms in smartphone selection, this research attempted to investigate the effectiveness of both methods in providing the analysis of criteria weight, the final recommendation weight, the product recommendation weight, and the execution time in DSS-SmartPhoneRec application development. A survey of one hundred respondents of University students identified the dominant criteria in selecting the smartphone, namely price, Random Access Memory (RAM), processor, internal memory, and camera. Hence, five alternative products were then chosen as the appropriate smartphones’ recommendations based on the respondent’s preferences. As an automatic tool, a DSS-SmartPhoneRec application was built to analyze and compare between F-AHP and F-ANP methods in resolving the smartphone selection cases. It revealed that the level of consistency of criteria weight, the final weight of recommendation, and the weight that the product-based F-ANP was 40% greater than F-AHP. In terms of execution time, F-AHP had a shorter time than F-ANP. Meanwhile, the comparison of products recommendation from DSS-SmartPhoneRec and a manual test showed that F-ANP was 16% more in line with the respondents’ predilection. In a nutshell, the DSS-SmartPhoneRec administered the devote smartphone recommendations based on the user’s expectation. The comparison analysis furnished a learning outcome for the users in determining the appropriate MADM method tailored to the type of cases.

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DOF: a generic approach of domain ontology fuzzification

Akremi

Zghal

2021

Front. Comput. Sci.

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Designing a Decision Support System for Recommending Smartphones Using Fuzzy Ontologies

Cited by 4 publications

References 23 publications

A survey on fuzzy ontologies for the Semantic Web

A survey on fuzzy ontologies for the Semantic Web

Decision Support System for Smartphone Recommendation: The Comparison of Fuzzy Ahp and Fuzzy Anp in Multi-Attribute Decision Making

DOF: a generic approach of domain ontology fuzzification

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