Stojanov, Riste
Preferred name
Stojanov, Riste
Official Name
Stojanov, Riste
Main Affiliation
Email
riste.stojanov@finki.ukim.mk
53 results
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Item type:Publication, Methodology for food prices forecasting(IEEE, 2023-12-15) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Web data mining of landslide information, an experimental study for Macedonia(Macedonian Association for Geotechnics, 2022-06); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Web data mining of landslide information, an experimental study for Macedonia(ISRM, 2022-06-23); The current paper introduces an innovative method to generate a landslide database utilising the possibilities offered by web data mining techniques. As sources of data are used social media networks and news aggregators, in combination with already available landslide databases of the authors. The algorithm of the data mining is explained, as well as the rules for relevant classification and recognition of landslide information. The territory of Macedonia is taken as a case study to test the method. Findings show that the approach can be very useful from various aspects, with main advantage being the swift landslide data collection and possibility for sharing among relevant institutions. The approach is also considered promising in regard to timely alarming of the population for expected danger from landslides. Certain aspects of possibilities for mining additional web content related to landslides are noted, as well as options for integration of the web mined landslide data with already existing databases and sharing among entities. Due to the advantage of language recognition during the data mining, the utilisation of the presented method is possible on both national and international level. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Temporal Authorization Graphs: Pros, Cons and Limits(Springer International Publishing, 2022-01); ; ; As more private data is entering the web, defining authorization about its access is crucial for privacy protection. This paper proposes a policy language that leverages SPARQL expressiveness and popularity for flexible access control management and enforces the protection using temporal graphs. The temporal graphs are created during the authentication phase and are cached for further usage. They enable design-time policy testing and debugging, which is necessary for correctness guarantee. The security never comes with convenience, and this paper examines the environments in which the temporal graphs are suitable. Based on the evaluation results, an approximated function is defined for suitability determination based on the expected temporal graph size. - Some of the metrics are blocked by yourconsent settings
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, Enhancing Text-Based Relatedness Measures with Semantic Web Data(Springer International Publishing, 2017-10-12); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning(2021-02-25); ; ; The challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This is due to the advances in neural network architectures, increase of computing power and the availability of diverse labeled datasets, which deliver pre-trained, highly accurate models. These tasks are generally focused on tagging common entities, but domain-specific use-cases require tagging custom entities which are not part of the pre-trained models. This can be solved by either fine-tuning the pre-trained models, or by training custom models. The main challenge lies in obtaining reliable labeled training and test datasets, and manual labeling would be a highly tedious task. In this paper we present PharmKE, a text analysis platform focused on the pharmaceutical domain, which applies deep learning through several stages for thorough semantic analysis of pharmaceutical articles. It performs text classification using state-of-the-art transfer learning models, and thoroughly integrates the results obtained through a proposed methodology. The methodology is used to create accurately labeled training and test datasets, which are then used to train models for custom entity labeling tasks, centered on the pharmaceutical domain. The obtained results are compared to the fine-tuned BERT and BioBERT models trained on the same dataset. Additionally, the PharmKE platform integrates the results obtained from named entity recognition tasks to resolve co-references of entities and analyze the semantic relations in every sentence, thus setting up a baseline for additional text analysis tasks, such as question answering and fact extraction. The recognized entities are also used to expand the knowledge graph generated by DBpedia Spotlight for a given pharmaceutical text. - 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, A comprehensive study of food prices and food fraud in the European Union(IEEE, 2023-12-15) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Assessing the Environmental Impact of Plant-Based Diets: A Comprehensive Analysis(IEEE, 2023-12-15)