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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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DTSTART:20231029T030000
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UID:calendar.26188.field_data.0@www.open.diag.uniroma1.it
DTSTAMP:20260405T150032Z
CREATED:20230707T170742Z
DESCRIPTION:Title: Addressing Model and Instance level Heterogeneities for 
 Data Preparation under Knowledge Representation Principles Abstract: Data 
 preparation is the process of collecting\, aggregating\, transforming\, an
 d cleaning raw data to prepare it for future processing and analysis. It i
 s a crucial process in all data-intensive applications\, such as analytics
  or those involving machine learning approaches that are sensitive to low 
 data quality\, thus impacting the final result. However\, data preparation
  often encounters challenges stemming from data-model heterogeneity\, requ
 iring the integration of sources beyond traditional structured databases. 
 Additionally\, data-level issues like entity duplication further complicat
 e the process. In this seminar\, I will delve into approaches rooted in kn
 owledge representation techniques and data integration principles to addre
 ss these challenges. The discussion will revolve around two key topics: 1)
  Extending the data integration paradigm known as Ontology-based data acce
 ss in order to incorporate semi-structured and unstructured sources (mainl
 y raw text). This extension aims to enhance the integration of diverse dat
 a sources\, facilitating a more comprehensive data preparation process. 2)
  Introducing a novel approach based on collective\, formal\, logical\, and
  reasoning-based methods to tackle the complex problem of entity resolutio
 n while also focusing on the query answering task. This approach aims to e
 nsure data accuracy and minimize redundancy by effectively addressing enti
 ty duplication. The main aim of the techniques that I will present is to i
 mprove the efficiency and effectiveness of data preparation\, ultimately e
 nhancing the overall quality and reliability of data analysis outcomes. Sh
 ort Bio: Federico Maria Scafoglieri serves as a PostDoc Researcher at the 
 Department of Computer\, Control\, and Management Engineering (DIAG) Anton
 io Ruberti at Sapienza University of Rome\, where he received a Ph.D. in E
 ngineering in Computer Science in 2022. His research focuses mainly on dat
 a management\, particularly in the areas of data integration and data qual
 ity. He has participated in various academic and industrial projects on th
 ese topics. During his PhD\, he was research scholar at IBM Research Almad
 en in California\, USA. He received the Best Demo Paper award at ISWC 2021
 .  
DTSTART;TZID=Europe/Paris:20230711T160000
DTEND;TZID=Europe/Paris:20230711T160000
LAST-MODIFIED:20230710T145316Z
LOCATION:Aula Magna
SUMMARY:Seminario pubblico di Federico Scafoglieri (Procedura valutativa pe
 r n.4 posti di Ricercatore a tempo determinato tipologia A - SC 09/H1 SSD 
 ING-INF/05) - Federico Scafoglieri
URL;TYPE=URI:http://www.open.diag.uniroma1.it/node/26188
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