Gramatikov, Sasho
Preferred name
Gramatikov, Sasho
Official Name
Gramatikov, Sasho
Main Affiliation
Email
sasho.gramatikov@finki.ukim.mk
33 results
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Item type:Publication, Learning Robust Food Ontology Alignment(IEEE, 2022-12-17); ; In today’s knowledge society, large number of information systems use many different individual schemes to represent data. Ontologies are a promising approach for formal knowledge representation and their number is growing rapidly. The semantic linking of these ontologies is a necessary prerequisite for establishing interoperability between the large number of services that structure the data with these ontologies. Consequently, the alignment of ontologies becomes a central issue when building a worldwide Semantic Web. There is a need to develop automatic or at least semi-automatic techniques to reduce the burden of manually creating and maintaining alignments. Ontologies are seen as a solution to data heterogeneity on the Web. However, the available ontologies are themselves a source of heterogeneity. On the Web, there are multiple ontologies that refer to the same domain, and with that comes the challenge of a given graph-based system using multiple ontologies whose taxonomy is different, but the semantics are the same. This can be overcome by aligning the ontologies or by finding the correspondence between their components.In this paper, we propose a method for indexing ontologies as a support to a solution for ontology alignment based on a neural network. In this process, for each semantic resource we combine the graph based representations from the RDF2vec model, together with the text representation from the BERT model in order to capture the semantic and structural features. This methodology is evaluated using the FoodOn and OntoFood ontologies, based on the Food Onto Map alignment dataset, which contains 155 unique and validly aligned resources. Using these limited resources, we managed to obtain accuracy of 74% and F1 score of 75% on the test set, which is a promising result that can be further improved in future. Furthermore, the methodology presented in this paper is both robust and ontology-agnostic. It can be applied to any ontology, regardless of the domain. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Semantic Sky: A Gmail Plugin for Email Classification and Annotation(2024-04); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Learning Robust Food Ontology Alignment(2023-01-26); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis and comparative evaluation of front-end technologies for web application development(2022) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparing the performance of Text Classification Models for climate change-related texts(Ss Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, Republic of North Macedonia, 2023-07); This study aims to evaluate and compare the performance of two text classification models specifically tailored for classifying climate change-related texts. The models under investigation are ClimateBert Environmental Claims and ClimateBert Fact Checking, both of which are based on the ClimateBert model and available in the HuggingFace Hub. Our analysis focuses on the impact of fine-tuning these models using specific climate change-related datasets, as well as their performance without fine-tuning. We assess the models using various metrics, including accuracy, precision, recall, and F1 score, and identify the areas where they predominantly make classification errors. Through our findings, we highlight the significance of using these methodologies for the evaluation and comparison of climate change-related text classification models and to appropriately fine-tune the models with context-specific data to achieve optimal classification results. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Popularity based distribution schemes for P2P assisted streaming of VoD contents(Telecomunicacion, 2012); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Last Mile Delivery with Autonomous Vehicles: Fiction or Reality?(Ss. Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, Republic of North Macedonia, 2019-05); ; ; Autonomous vehicles (AVs) are a disruptive technology of the 21-st century that are beginning the next revolution of the transportation of people and goods. Their presence has a particular impact on the future directions of development of E-commerce. The number of online orders is in a steep incline, and so is the necessity to deliver goods to the customer in an efficient and environmental friendly way. Using autonomous drones, pods and vans for delivery of goods has already become reality. But, what is the state of the art of the companies offering these services and how do people feel about it? The aim of this paper is to make an overview of the business models of the companies developing AVs for Last Mile Delivery (LMD) of goods and to find out what is the attitudes of the online customers towards using AVs for delivery of their goods. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Performance Evaluation of Back-end Web Application Programming Languages(Ss Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, Republic of North Macedonia, 2023-07) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Named Entity Recognition For Macedonian Language(CIIT 2021, 2021-05-06); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multi-horizon air pollution forecasting with deep neural networks(MDPI, 2021-02-10); ;