Ontology and semantic matching for diabetic food recommendations

Arwan, Achmad; Priyambadha, Bayu; Sarno, Riyanarto; Sidiq, Mohamad; Kristianto, Heri

doi:10.1109/iciteed.2013.6676233

Cited by 24 publications

(12 citation statements)

References 4 publications

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“…Ontology is one of the formal conceptual and certain knowledge possessed by a group of people to process automatically [1] [6]. Ontology is the theory of the meaning of an object, belongs to an object, and the object relation that may occur in the realm of knowledge.…”

Section: A Ontologymentioning

confidence: 99%

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Reusability ontology in business processes with similarity matching

2019

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Section: A Ontologymentioning

confidence: 99%

“…Secondly as a body of knowledge to explain a particular topic of Figure 1. Ontology is explained by using concept notation, examples, relations, functions, and axioms [1]. The world wide web consortium makes an ontology language that can be used for knowledge representation.…”

Section: A Ontologymentioning

confidence: 99%

Reusability ontology in business processes with similarity matching

2019

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“…Accuracy is a method to measure the classification quality of a system to the predicted results made by experts [11]. The expert results being the results of manual processes then become the basis for comparison with the classification results of the system.…”

Section: Test Data Classification Using Ga and Naive Bayesmentioning

confidence: 99%

Optimization of Genetic Algorithm Performance Using Naïve Bayes for Basis Path Generation

Arwan

Rusdianto

2017

KINETIK

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Basis path testing is a method used to identify code defects. The determination of independent paths on basis path testing can be generated by using Genetic Algorithm. However, this method has a weakness. In example, the number of iterations can affect the emersion of basis path. When the iteration is low, it results in the incomplete path occurences. Conversely, if iteration is plentiful resulting to path occurences, after a certain iteration, unfortunately, the result does not change. This study aims to perform the optimization of Genetic Algorithm performance for independent path determination by determining how many iteration levels match the characteristics of the code. The characteristics of the code used include Node, Edge, VG, NBD, and LOC. Moreover, Naïve Bayes is a method used to predict the exact number of iterations based on 17 selected code data into training data, and 16 data into test data. The result of system accuracy test is able to predict the exact iteration of 93.75% from 16 test data. Time-test results show that the new system was able to complete an independent search path being faster 15% than the old system.

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“…Easily, the average fuzzy rating issued by different users is treated as the inherent quality of an item t ∈ T , denoting as r t , referring to Eq. (5).…”

Section: A Fuzzy Rating Predictionmentioning

confidence: 99%

“…To handle the tree-structured taxonomy information, previous studies mainly employ tree-matching techniques such as tree similarity measure, tree isomorphism, and sub-tree matching to establish a kinds of semantic similarities between item to items or user to items. For example, in the food recommender system for diabetes patients developed by Arwan et al [5], both items (the food menus) and users (the patients) are represented by weighted trees with the same structure to denoting the nutrition supply and demand of foods and patients, respectively. Based on the construct ontology of users, foods, and nutrition taxonomy, the similarities of patient-to-patient and food-to-food are calculable so that new diet suggestions can be produced based on previous successful cases.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy content matching-based e-Commerce recommendation approach

Mao

Zhang

et al. 2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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E-Commerce products often come with rich and tree-structured content information describing the attributes. To well utilize the content information, this study proposed a fuzzy content matching-based recommendation approach to assist e-Commerce customers to choose their truly interested items. In this paper, users' ratings and preferences are represented using fuzzy numbers to remain uncertainties. Tree-structured content information is transformed to a set of descriptors, and users' preferences on these descriptors are derived from fuzzy ratings by using fuzzy number operations. A kind of preference dependence relations is established between descriptors to explore the relations of different content features, and as a base to sketch the complete profile of users. While the extended preference profile of a user is established, given a new item, the fuzzy match degree of the user preference and the item content information is carried out, and then a fuzzy Topsis ranking method is proposed to able to rank all candidate items according to the fuzzy match degrees, and the highest ranked items are recommended to the target user. We conduct empirical experiments on Yelp and MovieLens datasets. The results indicate that the proposed approach improve recommendation performance in terms of both coverage and accuracy.

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Ontology and semantic matching for diabetic food recommendations

Cited by 24 publications

References 4 publications

Reusability ontology in business processes with similarity matching

Reusability ontology in business processes with similarity matching

Optimization of Genetic Algorithm Performance Using Naïve Bayes for Basis Path Generation

A fuzzy content matching-based e-Commerce recommendation approach

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