Diet assessment based on type-2 fuzzy ontology and fuzzy markup language

Lee, Chang-Shing; Wang, Mei-Hui; Acampora, Giovanni; Hsu, Chin-Yuan; Hagras, Hani

doi:10.1002/int.20449

Cited by 66 publications

(39 citation statements)

References 40 publications

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“…For example, the study can be extended to a more general model that incorporates multiple minimum supports, weights of items, quantitative database (DeGraaf et al 2001), and even more complicated fuzzy ontological structures (Lee et al 2010) such as that exploiting both classification and component relationships, and the representation of the fuzzy ontology, e.g., Fuzzy Markup Language . Another prospective avenue is on embedding the frequent pattern maintenance scheme into an online data mining platform.…”

Section: Discussionmentioning

confidence: 99%

Updating generalized association rules with evolving fuzzy taxonomies

Lin

Tseng

2011

Soft Comput

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Mining generalized association rules with fuzzy taxonomic structures has been recognized as an important extension of generalized associations mining problem. To date most work on this problem, however, required the taxonomies to be static, ignoring the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from one hierarchy tree to another for more suitable classification, abandoned from the taxonomies if they will no longer be produced, or added into the taxonomies as new items. Additionally, the membership degrees expressing the fuzzy classification may also need to be adjusted. Under these circumstances, effectively updating the discovered generalized association rules is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called FDiff_ET and FDiff_ET*, to update the discovered generalized frequent itemsets. Empirical evaluations show that our algorithms can maintain their performance even in high degree of taxonomy evolution, and are significantly faster than applying the contemporary fuzzy generalized association mining algorithm FGAR to the database with evolving taxonomy.

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Section: Discussionmentioning

confidence: 99%

Updating generalized association rules with evolving fuzzy taxonomies

Lin

Tseng

2011

Soft Comput

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“…1,2 Ontologies-based systems organize complex information by means of graphs of concepts, each with their attributes, and describe relationships among concepts. By using this approach, artificial systems can better understand the data meaningfully and therefore more intelligently locate and integrate information for different kinds of tasks.…”

Section: Introductionmentioning

confidence: 99%

A hybrid evolutionary approach for solving the ontology alignment problem

Acampora

Loia

Salerno

et al. 2012

Int. J. Intell. Syst.

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Ontologies are recognized as a fundamental component for enabling interoperability across heterogeneous systems and applications. Indeed, they try to fit a common understanding of concepts in a particular domain of interest to support the exchange of information among people, artificial agents, and distributed applications. Unfortunately, because of human subjectivity, various ontologies related to the same application domain may use different terms for the same meaning or may use the same term to mean different things, raising the so-called heterogeneity problem. The ontology alignment process tries to solve this semantic gap by individuating a collection of similar entities belonging to different ontologies and enabling a full comprehension among different actors involved in a given knowledge exchanging. However, the complexity of the alignment task, especially for large ontologies, requires an automated and effective support for computing high-quality alignments. The aim of this paper is to propose a memetic algorithm to perform an efficient matching process capable of computing a suboptimal alignment between two ontologies. As shown by experiments, the memetic approach is more suitable for ontology alignment problem than a classical evolutionary technique such as genetic algorithms. © 2012 Wiley Periodicals, Inc

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“…Accordingly, Lee et al [83] used type-1 fuzzy logic to calculate the calorie allowance in an intelligent ontological agent for diabetic food recommendation. In their following studies, they have further developed their system to involve type-2 fuzzy ontology [84] as well as Fuzzy Markup Language [85]. However, these studies concentrated on the diabetics as a health condition and not on the user's mood, appetite and spare time which are quite crucial in deciding what to eat.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned previously, most approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices [74]- [78]. In addition, the existing applications such as the 'recipe recommenders' mentioned in [61]- [64], [81], [83]- [85] provide limited personalization features while neglecting the notion of adaptation and they also require the use of intrusive user interfaces. Hence, there is a need to enhance transparency in AIEs (especially when dealing with disabled and elderly people) by developing more natural ways of interaction which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings.…”

Section: Introductionmentioning

confidence: 99%

A Linear General Type-2 Fuzzy-Logic-Based Computing With Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation

Bilgin

Hagras

Helvert

et al. 2016

IEEE Trans. Fuzzy Syst.

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Abstract-This paper addresses the need to enhance transparency in Ambient Intelligent Environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient Intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent and context aware manner in order to enhance users' comfort. It is therefore appropriate for employing the paradigm of 'Computing With Words' (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite and spare time. The proposed CWWs framework is based on Linear General Type-2 (LGT2) Fuzzy Sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2 based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human-machine interaction. improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users' feedback; and the qualitative analysis presents social science evaluation confirming the strong user acceptance of the system. Index Terms-ambient intelligence, computing with words, general type-2 fuzzy sets I. INTRODUCTIONhe recent years have witnessed a rapid increase in the miniaturization of computers which enabled to embed computing throughout our spaces in familiar objects such as home appliances (e.g. washing machines, refrigerators, etc.), portable devices (e.g. mobile phones, tablets, etc.), cars, etc. In addition, the advances in communications allowed such devices to be networked and connected to the Internet. Among the highlights of the connected and miniaturized devices bring to real life is the opportunity of customization and therefore personalization. Lately, it is getting more important to deliver personalized content in areas such as food planning which is becoming an important personal issue that affects the ...

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Diet assessment based on type-2 fuzzy ontology and fuzzy markup language

Cited by 66 publications

References 40 publications

Updating generalized association rules with evolving fuzzy taxonomies

Updating generalized association rules with evolving fuzzy taxonomies

A hybrid evolutionary approach for solving the ontology alignment problem

A Linear General Type-2 Fuzzy-Logic-Based Computing With Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation

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