Semantic Analysis of Big Data in the Problem of Forecasting the Innovative Development of the Energy Infrastructure of the Russian Federation

2020 ◽  
Author(s):  
Alexey Kopaygorodsky ◽  
I. Khayrullin ◽  
E. Khayrullina
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Russian Federation ◽  
Semantic Analysis ◽  
Innovative Development ◽  
Energy Infrastructure ◽  
Automate Monitoring ◽  
The Russian Federation

The article discusses the use of methods of semantic analysis and natural language processing to support research and forecasting the innovative development of the energy infrastructure of the Russian Federation. The existing methods and approaches to the organization of monitoring of technological solutions and innovative scientific developments are considered. To automate monitoring, the authors propose the use of natural language processing (NLP) methods. Semantic analysis and knowledge integration are based on a system of ontologies. The paper presents the main methods and approaches to building an infrastructure for processing open Big Data. Application of the proposed methods makes it possible to improve the quality of scientific research in this area and make them better.

scholarly journals Natural Language Processing for Forecasting Innovative Development of the Energy Infrastructure

2020 ◽  
Vol 209 ◽  
pp. 03015
Author(s):  
Alex Kopaygorodsky
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Knowledge Integration ◽  
Semantic Analysis ◽  
Intelligent System ◽  
Scientific Research ◽  
Innovative Development ◽  
Energy Infrastructure

The article deals with the application of natural language processing methods to support research and forecasting the innovative development of energy infrastructure. The main methods of NLP, which are used to build an intelligent system to support scientific research, are considered. Methods of building infrastructure for processing Open Linked Data and Big Data are described. Semantic analysis and knowledge integration are based on ontology system. Applying suggested methods allow increasing quality of scientific research in this area and make it more effectively

Ansätze zur quantitativen Inhaltsanalyse

2021 ◽  
Vol 50 (2-3) ◽  
pp. 17-22
Author(s):  
Johannes Brunzel
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing

Der Beitrag erläutert, inwiefern die Methode der quantitativen Textanalyse ein wesentliches Mittel zur betriebswirtschaftlichen Effizienzsteigerung sein kann. Dabei geht der Artikel über die Nennung von Chancen und Risiken des Einsatzes von künstlicher Intelligenz/Big Data-Analysen hinaus, indem der Beitrag praxisorientiert wichtige Entwicklungen im Bereich der quantitativen Inhaltsanalyse aus der wirtschaftswissenschaftlichen Literatur herleitet. Nachfolgend unterteilt der Artikel die wichtigsten Schritte zur Implementierung in (1) Datenerhebung von quantitativen Textdaten, (2) Durchführung der generischen Textanalyse und (3) Durchführung des Natural Language Processing. Als ein Hauptergebnis hält der Artikel fest, dass Natural Language Processing-Ansätze zwar weiterführende und komplexere Einsichten bieten, jedoch das Potenzial generischer Textanalyse - aufgrund der Flexibilität und verhältnismäßig einfachen Anwendbarkeit im Unternehmenskontext - noch nicht ausgeschöpft ist. Zudem stehen Führungskräfte vor der dichotomen Entscheidung, ob programmierbasierte oder kommerzielle Lösungen für die Durchführung der Textanalyse relevant sind.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

Big Data and Natural Language Processing for Analysing Railway Safety

2018 ◽  
pp. 240-267
Author(s):  
Kanza Noor Syeda ◽  
Syed Noorulhassan Shirazi ◽  
Syed Asad Ali Naqvi ◽  
Howard J Parkinson ◽  
Gary Bamford
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Machine Intelligence ◽  
Data Availability ◽  
Accident Data ◽  
Data Driven Approach ◽  
Advanced Analytics ◽  
The Uk

Due to modern powerful computing and the explosion in data availability and advanced analytics, there should be opportunities to use a Big Data approach to proactively identify high risk scenarios on the railway. In this chapter, we comprehend the need for developing machine intelligence to identify heightened risk on the railway. In doing so, we have explained a potential for a new data driven approach in the railway, we then focus the rest of the chapter on Natural Language Processing (NLP) and its potential for analysing accident data. We review and analyse investigation reports of railway accidents in the UK, published by the Rail Accident Investigation Branch (RAIB), aiming to reveal the presence of entities which are informative of causes and failures such as human, technical and external. We give an overview of a framework based on NLP and machine learning to analyse the raw text from RAIB reports which would assist the risk and incident analysis experts to study causal relationship between causes and failures towards the overall safety in the rail industry.

scholarly journals The Rise of Big Data Science: A Survey of Techniques, Methods and Approaches in the Field of Natural Language Processing and Network Theory

2018 ◽  
Vol 2 (3) ◽  
pp. 22 ◽  
Author(s):  
Jeffrey Ray ◽  
Olayinka Johnny ◽  
Marcello Trovati ◽  
Stelios Sotiriadis ◽  
Nik Bessis
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Network Theory ◽  
Data Science ◽  
Theoretical Foundation ◽  
Scientific Field ◽  
Research Challenges ◽  
New Research

The continuous creation of data has posed new research challenges due to its complexity, diversity and volume. Consequently, Big Data has increasingly become a fully recognised scientific field. This article provides an overview of the current research efforts in Big Data science, with particular emphasis on its applications, as well as theoretical foundation.

scholarly journals Predicting Verification Methods from Natural Language Requirements

2020 ◽  
Author(s):  
Michael Prendergast
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Analysis ◽  
Nearest Neighbor ◽  
Large Set ◽  
Technical Requirements ◽  
Verification Methods ◽  
Natural Language Requirements ◽  
Processing Algorithms

Abstract – A Verification Cross-Reference Matrix (VCRM) is a table that depicts the verification methods for requirements in a specification. Usually requirement labels are rows, available test methods are columns, and an “X” in a cell indicates usage of a verification method for that requirement. Verification methods include Demonstration, Inspection, Analysis and Test, and sometimes Certification, Similarity and/or Analogy. VCRMs enable acquirers and stakeholders to quickly understand how a product’s requirements will be tested.Maintaining consistency of very large VCRMs can be challenging, and inconsistent verification methods can result in a large set of uncoordinated “spaghetti tests”. Natural language processing algorithms that can identify similarities between requirements offer promise in addressing this challenge.This paper applies and compares compares four natural language processing algorithms to the problem of automatically populating VCRMs from natural language requirements: Naïve Bayesian inference, (b) Nearest Neighbor by weighted Dice similarity, (c) Nearest Neighbor with Latent Semantic Analysis similarity, and (d) an ensemble method combining the first three approaches. The VCRMs used for this study are for slot machine technical requirements derived from gaming regulations from the countries of Australia and New Zealand, the province of Nova Scotia (Canada), the state of Michigan (United States) and recommendations from the International Association of Gaming Regulators (IAGR).

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