1,073 research outputs found
Developing an Arabic Infectious Disease Ontology to Include Non-Standard Terminology
Building ontologies is a crucial part of the semantic web endeavour. In recent years, research interest has grown rapidly in supporting languages such as Arabic in NLP in general but there has been very little research on medical ontologies for Arabic. We present a new Arabic ontology in the infectious disease domain to support various important applications including the monitoring of infectious disease spread via social media. This ontology meaningfully integrates the scientific vocabularies of infectious diseases with their informal equivalents. We use ontology learning strategies with manual checking to build the ontology. We applied three statistical methods for term extraction from selected Arabic infectious diseases articles: TF-IDF, C-value, and YAKE. We also conducted a study, by consulting around 100 individuals, to discover the informal terms related to infectious diseases in Arabic. In future work, we will automatically extract the relations for infectious disease concepts but for now these are manually created. We report two complementary experiments to evaluate the ontology. First, a quantitative evaluation of the term extraction results and an additional qualitative evaluation by a domain expert
Using Arabic Twitter to support analysis of the spread of Infectious Diseases
This study investigates how to use Arabic social media content, especially Twitter, to measure the incidence of infectious diseases. People use social media applications such as Twitter to find news related to diseases and/or express their opinions and feelings about them. As a result, a vast amount of information could be exploited by NLP researchers for a myriad of analyses despite the informal nature of social media writing style. Systematic monitoring of social media posts (infodemiology or infoveillance) could be useful to detect misinformation outbreaks as well as to reduce reporting lag time and to provide an independent complementary source of data compared with traditional surveillance approaches. However, there has been a lack of research about analysing Arabic tweets for health surveillance purposes, due to the lack of Arabic social media datasets in comparison with what is available for English and some other languages. Therefore, it is necessary for us to create our own corpus. In addition, building ontologies is a crucial part of the semantic web endeavour. In recent years, research interest has grown rapidly in supporting languages such as Arabic in NLP in general but there has been very little research on medical ontologies for Arabic. In this thesis, the first and the largest Arabic Twitter dataset in the area of health surveillance was created to use in training and testing in the research studies presented. The Machine Learning algorithms with NLP techniques especially for Arabic were used to classify tweets into five categories: academic, media, government, health professional, and the public, to assist in reliability and trust judgements by taking into account the source of the information alongside the content of tweets. An Arabic Infectious Diseases Ontology was presented and evaluated as part of a new method to bridge between formal and informal descriptions of Infectious Diseases. Different qualitative and quantitative studies were performed to analyse Arabic tweets that have been written during the pandemic, i.e. COVID-19, to show how Public Health Organisations can learn from social media. A system was presented that measures the spread of two infectious diseases based on our Ontology to illustrate what quantitative patterns and qualitative themes can be extracted
COVID-19 and Arabic Twitter:How can Arab World Governments and Public Health Organizations Learn from Social Media?
In March 2020, the World Health Organization announced the COVID-19 outbreak as a pandemic. Most previous social media related research has been on English tweets and COVID-19. In this study, we collect approximately 1 million Arabic tweets from the Twitter streaming API related to COVID-19. Focussing on outcomes that we believe will be useful for Public Health Organizations, we analyse them in three different ways: identifying the topics discussed during the period, detecting rumours, and predicting the source of the tweets. We use the k-means algorithm for the first goal with k=5. The topics discussed can be grouped as follows: COVID-19 statistics, prayers for God, COVID-19 locations, advise and education for prevention, and advertising. We sample 2000 tweets and label them manually for false information, correct information, and unrelated. Then, we apply three different machine learning algorithms, Logistic Regression, Support Vector Classification, and NaΓ―ve Bayes with two sets of features, word frequency approach and word embeddings. We find that Machine Learning classifiers are able to correctly identify the rumour related tweets with 84% accuracy. We also try to predict the source of the rumour related tweets depending on our previous model which is about classifying tweets into five categories: academic, media, government, health professional, and public. Around (60%) of the rumour related tweets are classified as written by health professionals and academics
Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHRβs Guideline Definition Language
BACKGROUND: Providing scalable clinical decision support (CDS) across institutions that use different electronic health record (EHR) systems has been a challenge for medical informatics researchers. The lack of commonly shared EHR models and terminology bindings has been recognised as a major barrier to sharing CDS content among different organisations. The openEHR Guideline Definition Language (GDL) expresses CDS content based on openEHR archetypes and can support any clinical terminologies or natural languages. Our aim was to explore in an experimental setting the practicability of GDL and its underlying archetype formalism. A further aim was to report on the artefacts produced by this new technological approach in this particular experiment. We modelled and automatically executed compliance checking rules from clinical practice guidelines for acute stroke care. METHODS: We extracted rules from the European clinical practice guidelines as well as from treatment contraindications for acute stroke care and represented them using GDL. Then we executed the rules retrospectively on 49 mock patient cases to check the casesβ compliance with the guidelines, and manually validated the execution results. We used openEHR archetypes, GDL rules, the openEHR reference information model, reference terminologies and the Data Archetype Definition Language. We utilised the open-sourced GDL Editor for authoring GDL rules, the international archetype repository for reusing archetypes, the open-sourced Ocean Archetype Editor for authoring or modifying archetypes and the CDS Workbench for executing GDL rules on patient data. RESULTS: We successfully represented clinical rules about 14 out of 19 contraindications for thrombolysis and other aspects of acute stroke care with 80 GDL rules. These rules are based on 14 reused international archetypes (one of which was modified), 2 newly created archetypes and 51 terminology bindings (to three terminologies). Our manual compliance checks for 49 mock patients were a complete match versus the automated compliance results. CONCLUSIONS: Shareable guideline knowledge for use in automated retrospective checking of guideline compliance may be achievable using GDL. Whether the same GDL rules can be used for at-the-point-of-care CDS remains unknown
Jordanian paediatric nurses' views on compliance with Standard Precautions : a qualitative study
INTRODUCTIONCompliance with evidence-based Standard Precautions Guidelines (SPGs) among healthcare practitioners is essential to combat Healthcare Associated Infections (HCAI). However, it is widely understood that non-compliance with these precautions remains a common problem in paediatric nursing practice. Most existing studies into this problem have used quantitative methods. However, these studies have failed to explain noncompliant behaviour or address the issues that are specific to paediatric clinical areas.AIMThis study is designed to investigate paediatric nurses' perceptions and experiences of infection control measures and to achieve a better understanding of the factors that influence nursesβ compliance with SPGs.METHODSThis qualitative study used an adapted constructivist grounded theory approach. The study was conducted in five Jordanian hospitals. Thirty one (n=31) qualified paediatric nurses from different paediatric areas were reccruited to the study. Data were gathered using face-to-face semi-structured audio-taped interviews, which were transcribed and coded through constant comparative analysis.RESULTSThis study identified causes of enduring failure by nurses to comply fully with SPGs. Four themes emerged (Children are different; Nurses are human first; Limited professional status; The challenges of the working environment). Paediatric nurses claim to be willing to comply with SPGs, but sometimes fail to achieve this. Risk of exposure to microorganisms was perceived as a major factor in compliance. Paediatric nursing practice was seen as different to adult practice and nurses construed the need for SPGs differently.DISCUSSIONA key issue is the fact that nurses were reluctant to see themselves as change-agents to improve practice. This resulted in problems with SPGs being well understood but not acted on. Nurseβs prioritised compliance with the nursing culture in their specific clinical area, over more general principles of care, such as SPGs. Nurses did appreciate that compliance with SPGs was suboptimal and did sometimes criticise this situation. However, most nurses had a value system, which militated against the proper use of Standard Precautions and which served to diminish the influence of them.IMPLICATIONThe chief implication of this study is that infection control is unlikely to improve further until nurses feel empowered to initiate change. Nursing in this area of the world is essentially semi-professional in nature. Nursing needs to develop to become fully professional in its orientation so that nurses take full responsibility for their actions. Only when nurses see their actions and behaviour as fully their responsibility, will nursing issues such as this be properly addressed. Until this occurs, the imposition of rules and guidelines, documentation and policies, will not be sufficient to progress care in this important area of practice
Design of a Controlled Language for Critical Infrastructures Protection
We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates
from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically
represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of
traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an
analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen
Developing an Islamic framework for psychotherapy: An Islamic conceptualization of psychological wellbeing and healing
Data from outcome research studies indicate that spiritual and religious approaches to psychotherapy are effective in improving the psychological wellbeing of clients. While there has been significant growth in the field of Islamic psychology, the development of an approach to counselling that is indigenous to Islamic thought and scholarly works is no simple task. The purpose of this study was to explore and develop the beginnings of a psychotherapeutic framework based upon the Islamic understanding of psychological wellbeing and healing. Using a modified Delphi method with Islamic scholars and teachers as participants (n=6), this study has demonstrated the application of a unique methodological approach applying Islamic epistemological and ontological principles. After three rounds of questionnaires using the Delphi method, emergent coding content analyses and quantitative analyses of the data resulted in 47 consensus statements on the Islamic views of human nature, psychological wellness and illness, and change processes. The major themes and findings of this study lay the groundwork for the development of a psychotherapeutic approach that can be used by counsellors and other helping professionals with both Muslim and non-Muslim clients. There is a need for further exploration, additional research, and multi-methodology studies to create a comprehensive and practical framework. The findings of this research ultimately further the collective effort in the field of Islamic psychology to develop an epistemologically and ontologically sound Islamic approach that can be applied in counselling practice
ΠΠΊΡΡΠΆΠ΅ΡΠ΅ Π·Π° Π°Π½Π°Π»ΠΈΠ·Ρ ΠΈ ΠΎΡΠ΅Π½Ρ ΠΊΠ²Π°Π»ΠΈΡΠ΅ΡΠ° Π²Π΅Π»ΠΈΠΊΠΈΡ ΠΈ ΠΏΠΎΠ²Π΅Π·Π°Π½ΠΈΡ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°
Linking and publishing data in the Linked Open Data format increases the interoperability
and discoverability of resources over the Web. To accomplish this, the process comprises
several design decisions, based on the Linked Data principles that, on one hand, recommend to
use standards for the representation and the access to data on the Web, and on the other hand
to set hyperlinks between data from different sources.
Despite the efforts of the World Wide Web Consortium (W3C), being the main international
standards organization for the World Wide Web, there is no one tailored formula for publishing
data as Linked Data. In addition, the quality of the published Linked Open Data (LOD) is a
fundamental issue, and it is yet to be thoroughly managed and considered.
In this doctoral thesis, the main objective is to design and implement a novel framework for
selecting, analyzing, converting, interlinking, and publishing data from diverse sources,
simultaneously paying great attention to quality assessment throughout all steps and modules
of the framework. The goal is to examine whether and to what extent are the Semantic Web
technologies applicable for merging data from different sources and enabling end-users to
obtain additional information that was not available in individual datasets, in addition to the
integration into the Semantic Web community space. Additionally, the Ph.D. thesis intends to
validate the applicability of the process in the specific and demanding use case, i.e. for creating
and publishing an Arabic Linked Drug Dataset, based on open drug datasets from selected
Arabic countries and to discuss the quality issues observed in the linked data life-cycle. To that
end, in this doctoral thesis, a Semantic Data Lake was established in the pharmaceutical domain
that allows further integration and developing different business services on top of the
integrated data sources. Through data representation in an open machine-readable format, the
approach offers an optimum solution for information and data dissemination for building
domain-specific applications, and to enrich and gain value from the original dataset. This thesis
showcases how the pharmaceutical domain benefits from the evolving research trends for
building competitive advantages. However, as it is elaborated in this thesis, a better
understanding of the specifics of the Arabic language is required to extend linked data
technologies utilization in targeted Arabic organizations.ΠΠΎΠ²Π΅Π·ΠΈΠ²Π°ΡΠ΅ ΠΈ ΠΎΠ±ΡΠ°Π²ΡΠΈΠ²Π°ΡΠ΅ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° Ρ ΡΠΎΡΠΌΠ°ΡΡ "ΠΠΎΠ²Π΅Π·Π°Π½ΠΈ ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΈ ΠΏΠΎΠ΄Π°ΡΠΈ" (Π΅Π½Π³.
Linked Open Data) ΠΏΠΎΠ²Π΅ΡΠ°Π²Π° ΠΈΠ½ΡΠ΅ΡΠΎΠΏΠ΅ΡΠ°Π±ΠΈΠ»Π½ΠΎΡΡ ΠΈ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡΠΈ Π·Π° ΠΏΡΠ΅ΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΡΠ΅ΡΡΡΡΠ°
ΠΏΡΠ΅ΠΊΠΎ Web-Π°. ΠΡΠΎΡΠ΅Ρ ΡΠ΅ Π·Π°ΡΠ½ΠΎΠ²Π°Π½ Π½Π° Linked Data ΠΏΡΠΈΠ½ΡΠΈΠΏΠΈΠΌΠ° (W3C, 2006) ΠΊΠΎΡΠΈ ΡΠ° ΡΠ΅Π΄Π½Π΅
ΡΡΡΠ°Π½Π΅ Π΅Π»Π°Π±ΠΎΡΠΈΡΠ° ΡΡΠ°Π½Π΄Π°ΡΠ΄Π΅ Π·Π° ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ°ΡΠ΅ ΠΈ ΠΏΡΠΈΡΡΡΠΏ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° Π½Π° WΠ΅Π±Ρ (RDF, OWL,
SPARQL), Π° ΡΠ° Π΄ΡΡΠ³Π΅ ΡΡΡΠ°Π½Π΅, ΠΏΡΠΈΠ½ΡΠΈΠΏΠΈ ΡΡΠ³Π΅ΡΠΈΡΡ ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ Ρ
ΠΈΠΏΠ΅ΡΠ²Π΅Π·Π° ΠΈΠ·ΠΌΠ΅ΡΡ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°
ΠΈΠ· ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΠΈΠ·Π²ΠΎΡΠ°.
Π£ΠΏΡΠΊΠΎΡ Π½Π°ΠΏΠΎΡΠΈΠΌΠ° W3C ΠΊΠΎΠ½Π·ΠΎΡΡΠΈΡΡΠΌΠ° (W3C ΡΠ΅ Π³Π»Π°Π²Π½Π° ΠΌΠ΅ΡΡΠ½Π°ΡΠΎΠ΄Π½Π° ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΠ° Π·Π°
ΡΡΠ°Π½Π΄Π°ΡΠ΄Π΅ Π·Π° Web-Ρ), Π½Π΅ ΠΏΠΎΡΡΠΎΡΠΈ ΡΠ΅Π΄ΠΈΠ½ΡΡΠ²Π΅Π½Π° ΡΠΎΡΠΌΡΠ»Π° Π·Π° ΠΈΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΡ ΠΏΡΠΎΡΠ΅ΡΠ°
ΠΎΠ±ΡΠ°Π²ΡΠΈΠ²Π°ΡΠ΅ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° Ρ Linked Data ΡΠΎΡΠΌΠ°ΡΡ. Π£Π·ΠΈΠΌΠ°ΡΡΡΠΈ Ρ ΠΎΠ±Π·ΠΈΡ Π΄Π° ΡΠ΅ ΠΊΠ²Π°Π»ΠΈΡΠ΅Ρ
ΠΎΠ±ΡΠ°Π²ΡΠ΅Π½ΠΈΡ
ΠΏΠΎΠ²Π΅Π·Π°Π½ΠΈΡ
ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΈΡ
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΠΎΠ΄Π»ΡΡΡΡΡΡΠΈ Π·Π° Π±ΡΠ΄ΡΡΠΈ ΡΠ°Π·Π²ΠΎΡ Web-Π°, Ρ ΠΎΠ²ΠΎΡ
Π΄ΠΎΠΊΡΠΎΡΡΠΊΠΎΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ, Π³Π»Π°Π²Π½ΠΈ ΡΠΈΡ ΡΠ΅ (1) Π΄ΠΈΠ·Π°ΡΠ½ ΠΈ ΠΈΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ° ΠΈΠ½ΠΎΠ²Π°ΡΠΈΠ²Π½ΠΎΠ³ ΠΎΠΊΠ²ΠΈΡΠ°
Π·Π° ΠΈΠ·Π±ΠΎΡ, Π°Π½Π°Π»ΠΈΠ·Ρ, ΠΊΠΎΠ½Π²Π΅ΡΠ·ΠΈΡΡ, ΠΌΠ΅ΡΡΡΠΎΠ±Π½ΠΎ ΠΏΠΎΠ²Π΅Π·ΠΈΠ²Π°ΡΠ΅ ΠΈ ΠΎΠ±ΡΠ°Π²ΡΠΈΠ²Π°ΡΠ΅ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΠΈΠ·
ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΠΈΠ·Π²ΠΎΡΠ° ΠΈ (2) Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΡΠΈΠΌΠ΅Π½Π° ΠΎΠ²ΠΎΠ³ ΠΏΡΠΈΡΡΡΠΏΠ° Ρ ΡΠ°ΡΠΌΠ°ΡeΡΡΡΠΊΠΎΠΌ Π΄ΠΎΠΌΠ΅Π½Ρ.
ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° Π΄ΠΎΠΊΡΠΎΡΡΠΊΠ° Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ° Π΄Π΅ΡΠ°ΡΠ½ΠΎ ΠΈΡΡΡΠ°ΠΆΡΡΠ΅ ΠΏΠΈΡΠ°ΡΠ΅ ΠΊΠ²Π°Π»ΠΈΡΠ΅ΡΠ° Π²Π΅Π»ΠΈΠΊΠΈΡ
ΠΈ
ΠΏΠΎΠ²Π΅Π·Π°Π½ΠΈΡ
Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° (Π΅Π½Π³. Linked Data Ecosystems), ΡΠ·ΠΈΠΌΠ°ΡΡΡΠΈ Ρ ΠΎΠ±Π·ΠΈΡ
ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ ΠΏΠΎΠ½ΠΎΠ²Π½ΠΎΠ³ ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ° ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΈΡ
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°. Π Π°Π΄ ΡΠ΅ ΠΌΠΎΡΠΈΠ²ΠΈΡΠ°Π½ ΠΏΠΎΡΡΠ΅Π±ΠΎΠΌ Π΄Π° ΡΠ΅
ΠΎΠΌΠΎΠ³ΡΡΠΈ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠΈΠΌΠ° ΠΈΠ· Π°ΡΠ°ΠΏΡΠΊΠΈΡ
Π·Π΅ΠΌΠ°ΡΠ° Π΄Π° ΡΠΏΠΎΡΡΠ΅Π±ΠΎΠΌ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠΈΡ
Π²Π΅Π± ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ°
ΠΏΠΎΠ²Π΅ΠΆΡ ΡΠ²ΠΎΡΠ΅ ΠΏΠΎΠ΄Π°ΡΠΊΠ΅ ΡΠ° ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΈΠΌ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ°, ΠΊΠ°ΠΎ Π½ΠΏΡ. DBpedia-ΡΠΎΠΌ. Π¦ΠΈΡ ΡΠ΅ Π΄Π° ΡΠ΅ ΠΈΡΠΏΠΈΡΠ°
Π΄Π° Π»ΠΈ ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΈ ΠΏΠΎΠ΄Π°ΡΠΈ ΠΈΠ· ΠΡΠ°ΠΏΡΠΊΠΈΡ
Π·Π΅ΠΌΠ°ΡΠ° ΠΎΠΌΠΎΠ³ΡΡΠ°Π²Π°ΡΡ ΠΊΡΠ°ΡΡΠΈΠΌ ΠΊΠΎΡΠΈΡΠ½ΠΈΡΠΈΠΌΠ° Π΄Π° Π΄ΠΎΠ±ΠΈΡΡ
Π΄ΠΎΠ΄Π°ΡΠ½Π΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΠ΅ ΠΊΠΎΡΠ΅ Π½ΠΈΡΡ Π΄ΠΎΡΡΡΠΏΠ½Π΅ Ρ ΠΏΠΎΡΠ΅Π΄ΠΈΠ½Π°ΡΠ½ΠΈΠΌ ΡΠΊΡΠΏΠΎΠ²ΠΈΠΌΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°, ΠΏΠΎΡΠ΅Π΄
ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΡΠ΅ Ρ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠΈ WΠ΅Π± ΠΏΡΠΎΡΡΠΎΡ.
ΠΠΎΠΊΡΠΎΡΡΠΊΠ° Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ° ΠΏΡΠ΅Π΄Π»Π°ΠΆΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡΡ Π·Π° ΡΠ°Π·Π²ΠΎΡ Π°ΠΏΠ»ΠΈΠΊΠ°ΡΠΈΡΠ΅ Π·Π° ΡΠ°Π΄ ΡΠ°
ΠΏΠΎΠ²Π΅Π·Π°Π½ΠΈΠΌ (Linked) ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° ΠΈ ΠΈΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠΈΡΠ° ΡΠΎΡΡΠ²Π΅ΡΡΠΊΠΎ ΡΠ΅ΡΠ΅ΡΠ΅ ΠΊΠΎΡΠ΅ ΠΎΠΌΠΎΠ³ΡΡΡΡΠ΅
ΠΏΡΠ΅ΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΠΊΠΎΠ½ΡΠΎΠ»ΠΈΠ΄ΠΎΠ²Π°Π½ΠΎΠ³ ΡΠΊΡΠΏΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΠΎ Π»Π΅ΠΊΠΎΠ²ΠΈΠΌΠ° ΠΈΠ· ΠΈΠ·Π°Π±ΡΠ°Π½ΠΈΡ
Π°ΡΠ°ΠΏΡΠΊΠΈΡ
Π·Π΅ΠΌΠ°ΡΠ°. ΠΠΎΠ½ΡΠΎΠ»ΠΈΠ΄ΠΎΠ²Π°Π½ΠΈ ΡΠΊΡΠΏ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΡΠ΅ ΠΈΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠΈΡΠ°Π½ Ρ ΠΎΠ±Π»ΠΈΠΊΡ Π‘Π΅ΠΌΠ°Π½ΡΠΈΡΠΊΠΎΠ³ ΡΠ΅Π·Π΅ΡΠ°
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° (Π΅Π½Π³. Semantic Data Lake).
ΠΠ²Π° ΡΠ΅Π·Π° ΠΏΠΎΠΊΠ°Π·ΡΡΠ΅ ΠΊΠ°ΠΊΠΎ ΡΠ°ΡΠΌΠ°ΡΠ΅ΡΡΡΠΊΠ° ΠΈΠ½Π΄ΡΡΡΡΠΈΡΠ° ΠΈΠΌΠ° ΠΊΠΎΡΠΈΡΡΠΈ ΠΎΠ΄ ΠΏΡΠΈΠΌΠ΅Π½Π΅
ΠΈΠ½ΠΎΠ²Π°ΡΠΈΠ²Π½ΠΈΡ
ΡΠ΅Ρ
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