Gorjan Popovski

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Conference papersshow recent

The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection, GECCO 2021
Personalizing Performance Regression Models to Black-Box Optimization Problems, GECCO 2021
Toward Robust Food Ontology Mapping, BFNDMA2020 at IEEE BigData 2020
BuTTER: BidirecTional LSTM for FoodNamed-Entity Recognition, BFNDMA2020 at IEEE BigData 2020
Ontology alignment using Named-Entity Recognition methods in the domain of food, IS 2020
An Insight into Food Semantics: Review, Analysis, and Lessons Learnt over Food-related Studies, ICBO 2020
FoodViz: Visualization of Food Entities Linked Across Different Standards, LOD 2020
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscape Space, IEEE SSCI 2020
Deep Statistics: More Robust Performance Statistics for Single-objective Optimization Benchmarking, GECCO 2020
performViz: A Machine Learning Approach to Visualize and Understand the Performance of Single-objective Optimization Algorithms, GECCO 2020
Performance2vec: A step further in explainable stochastic optimization algorithms performance, GECCO 2020
Food Data Integration by Using Heuristics based on Lexical and Semantic Similarities, HEALTHINF 2020
Exploring a standardized language for describing foods using embedding techniques, BFNDMA 2019 at IEEE BigData 2019
FoodOntoMap: Linking Food Concepts across different Food Ontologies, KEOD 2019
FoodIE: a rule-based named-entity recognition for food information extraction, ICPRAM 2019

Books & Chaptersshow all

FoodOntoMap: Linking Food Concepts across different Food Ontologies

G. Popovski, B. Koroušić Seljak, T. Eftimov
Proc. Knowledge Discovery and Ontology Development
Vienna, Austria: 17-19 September 2019

abstract:
In the last decade, a great amount of work has been done in predictive modelling in healthcare. All this work is made possible by the existence of several available biomedical vocabularies and standards, which play a crucial role in understanding health information. Moreover, there are available systems, such as the Unified Medical Language System, that bring and link together all these biomedical vocabularies to enable interoperability between computer systems. However, in 2019, Lancet Planetary Health published that the year 2019 is going to be the year of nutrition, where the focus will be on the links between food systems, human health, and the environment. While there is a large number of available resources for the biomedical domain, only a limited number of resources can be utilized in the food domain. There is still no annotated corpus with food concepts, and there are only a few rule-based food named-entity recognition systems for food concepts extraction. There are also several food ontologies that exist, each developed for a specific application scenario. However there are no links between these ontologies. For this reason, we have created a FoodOntoMap resource that consists of food concepts extracted from recipes. For each food concept, semantic tags from four food ontologies are assigned. With this, we have created a resource that provides a link between different food ontologies that can be further reused to develop applications for understanding the relation between food systems, human health, and the environment.