JITA

JITA Journal of Information Technology and Applications

Vol. 1 No. 1 (2011): JITA - APEIRON

Bojan Furlan, Vladimir Sivački, Davor Jovanović, Boško Nikolić

Comparable Evaluation of Contemporary Corpus-Based and Knowledge-Based Semantic Similarity Measures of Short Texts

Original scientific paper

DOI: https://doi.org/10.7251/JIT1101065F

Abstract

This paper presents methods for measuring the semantic similarity of texts, where we evaluated different approaches based on existing similarity measures. On one side word similarity was calculated by processing large text corpuses and on the other, commonsense knowledgebase was used. Given that a large fraction of the information available today, on the Web and elsewhere, consists of short text snippets (e.g. abstracts of scientific documents, image captions or product descriptions), where commonsense knowledge has an important role, in this paper we focus on computing the similarity between two sentences or two short paragraphs by extending existing measures with information from the ConceptNet knowledgebase. On the other hand, an extensive research has been done in the field of corpus-based semantic similarity, so we also evaluated existing solutions by imposing some modifications. Through experiments performed on a paraphrase data set, we demonstrate that some of proposed approaches can improve the semantic similarity measurement of short text.

Keywords: semantic similarity, corpus-based, knowledge-based.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.