Publication:
An ontology based semantic representation for Turkish Cuisine, Türk mutfaǧi için ontoloji tabanli semantik gösterim

dc.contributor.authorErgün, Övgü Öztürk
dc.contributor.authorÖztürk, Bengü
dc.contributor.institutionErgün, Övgü Öztürk, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey
dc.contributor.institutionÖztürk, Bengü, İnönü Mah, Yeditepe University, Istanbul, Turkey
dc.date.accessioned2025-10-05T16:08:04Z
dc.date.issued2018
dc.description.abstractFollowing recent advances in digital technologies, many data in various domains have been transformed into digital world and shared with millions of users via social media and web technologies. As a result, big amount of data has presented many challenging problems in different fields, e.g internet of things, artificial intelligence. One of application areas is in food domain. Recognition of food category from images, automatic recipe retrieval from internet and analysis and matching of food images with recipes, ingredients, nutrition values bring cooperation of multi disciplines and technologies. In this work, for the first time, semantical analysis of Turkish Cuisine is held and various information related to food in Turkish Cuisine is structured in a hierarchical ontology model. A new database containing 50 different food categories and related images is constructed and linked with data such as food properties, recipes, etc. As a result, multimodal information retrieval can be achieved faster in a more semantic way. At the same time, food image classification with deep learning methods is performed and faster connection of recognized food category to related semantic data is provided. © 2018 Elsevier B.V., All rights reserved.
dc.description.sponsorshipAselsan
dc.description.sponsorshipet al.
dc.description.sponsorshipHuawei
dc.description.sponsorshipIEEE Signal Processing Society
dc.description.sponsorshipIEEE Turkey Section
dc.description.sponsorshipNetas
dc.identifier.conferenceName26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
dc.identifier.conferencePlaceIzmir, Altin Yunus Resort ve Thermal Hotel
dc.identifier.doi10.1109/SIU.2018.8404617
dc.identifier.endpage4
dc.identifier.isbn9781538615010
dc.identifier.scopus2-s2.0-85050817131
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1109/SIU.2018.8404617
dc.identifier.urihttps://hdl.handle.net/20.500.14719/11607
dc.language.isotr
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subject.authorkeywordsDeep Learning
dc.subject.authorkeywordsFood Classificiation
dc.subject.authorkeywordsOntology
dc.subject.authorkeywordsPattern Recognition
dc.subject.authorkeywordsSemantic Data
dc.subject.authorkeywordsTurkish Cuisine
dc.subject.authorkeywordsDeep Learning
dc.subject.authorkeywordsOntology
dc.subject.authorkeywordsPattern Recognition
dc.subject.authorkeywordsSemantics
dc.subject.authorkeywordsApplication Area
dc.subject.authorkeywordsDigital Technologies
dc.subject.authorkeywordsLearning Methods
dc.subject.authorkeywordsMulti Disciplines
dc.subject.authorkeywordsMultimodal Information Retrieval
dc.subject.authorkeywordsSemantic Data
dc.subject.authorkeywordsSemantic Representation
dc.subject.authorkeywordsTurkishs
dc.subject.authorkeywordsSignal Processing
dc.subject.indexkeywordsDeep learning
dc.subject.indexkeywordsOntology
dc.subject.indexkeywordsPattern recognition
dc.subject.indexkeywordsSemantics
dc.subject.indexkeywordsApplication area
dc.subject.indexkeywordsDigital technologies
dc.subject.indexkeywordsLearning methods
dc.subject.indexkeywordsMulti disciplines
dc.subject.indexkeywordsMultimodal information retrieval
dc.subject.indexkeywordsSemantic data
dc.subject.indexkeywordsSemantic representation
dc.subject.indexkeywordsTurkishs
dc.subject.indexkeywordsSignal processing
dc.titleAn ontology based semantic representation for Turkish Cuisine, Türk mutfaǧi için ontoloji tabanli semantik gösterim
dc.typeConference Paper
dcterms.referencesChen, Jingjing, Deep-based ingredient recognition for cooking recipe retrieval, pp. 32-41, (2016), Salvador, Amaia, Learning cross-modal embeddings for cooking recipes and food images, 2017-January, pp. 3068-3076, (2017), IEEE Transactions on Cybernetics, (2017), Çelik Ertuğrul, Duygu, FoodWiki: a Mobile App Examines Side Effects of Food Additives Via Semantic Web, Journal of Medical Systems, 40, 2, pp. 1-15, (2016), Gungor, Cem, Turkish cuisine: A benchmark dataset with Turkish meals for food recognition, (2017), Chu, Wei Ta, Food image description based on deep-based joint food category, ingredient, and cooking method recognition, pp. 109-114, (2017), Ciocca, Gianluigi, Food Recognition: A New Dataset, Experiments, and Results, IEEE Journal of Biomedical and Health Informatics, 21, 3, pp. 588-598, (2017), Mezgec, Simon, Nutrinet: A deep learning food and drink image recognition system for dietary assessment, Nutrients, 9, 7, (2017), Hassannejad, Hamid, Food image recognition using very deep convolutional networks, pp. 41-49, (2016), Myers, Austin, Im2Calories: Towards an automated mobile vision food diary, Proceedings of the IEEE International Conference on Computer Vision, 2015 International Conference on Computer Vision, ICCV 2015, pp. 1233-1241, (2015)
dspace.entity.typePublication
local.indexed.atScopus
person.identifier.scopus-author-id55292851100
person.identifier.scopus-author-id26433711800

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