Skip to main content

Paolo Ceravolo

Università degli Studi di Milano - State University of Milan (Italy), DIPARTIMENTO DI TECNOLOGIE DELL'INFORMAZIONE, Faculty Member

Followers

77

Following

62

Co-authors

6

Public Views

InterestsView All (19)

Uploads

Papers by Paolo Ceravolo

A Framework for Semi-Automatic Data Integration

by Marcello Leida and Paolo Ceravolo

Enterprise Information …, Jan 1, 2009

Recent studies on Business Intelligence highlights the need of on-time, trustable and sound data ... more Recent studies on Business Intelligence highlights the need of on-time, trustable and sound data access systems. Moreover the application of these systems in a flexible and dynamic environment requires for an approach based on automatic procedures that can provide reliable results. A crucial factor for any automatic data integration system is the matching process. Different categories of matching operators carry different semantics. For this reason combining them in a single algorithm is a non trivial process that have to take into account ...

OntoExtractor: a Fuzzy-Based Approach to Content and Structure-Based Metadata Extraction

by Marcello Leida and Paolo Ceravolo

On the Move to Meaningful …, Jan 1, 2006

An FCA-Based Mapping Generator

by Marcello Leida and Paolo Ceravolo

… and Factory Automation, …, Jan 1, 2008

We present an overview of ODDI an Ontology Driven Data Integration system based on Formal concept... more

Semantics-Aware Matching Strategy (SAMS) for the Ontology MeDiated Data Integration (ODDI)

by Marcello Leida and Paolo Ceravolo

International Journal of …, Jan 1, 2010

Data integration systems are used to integrate heterogeneous data sources in a single view. Recen... more Data integration systems are used to integrate heterogeneous data sources in a single view. Recent work on business intelligence highlights the need of on-time, reliable and sound data access systems relying on methods based on semi-automatic procedures. A crucial factor for any semi-automatic algorithm is that of the matching strategy. Different categories of matching operators carry different semantics. For this reason, combining them into a single strategy is a non-trivial process that has to take into account a variety of options. ...

ODDI: a Framework for Semi-Automatic Data Integration

by Marcello Leida and Paolo Ceravolo

ICEIS 2008: proceedings of the …, Jan 1, 2008

Which Role for An Ontology of Uncertainty?

by Marcello Leida and Paolo Ceravolo

Matching Operators In Data Integration

by Marcello Leida and Paolo Ceravolo

This paper discusses the role of matching operators in Data Integration process. The paper propos... more This paper discusses the role of matching operators in Data Integration process. The paper proposes acategorization of matching operators, with the aim of distinguishing different relations to the possiblegoals of an integration process and their impact on the implementation of the mappings defining thisintegration. In order to lead the discussion some example.

Ontology Robustness In Evolution

by Marcello Leida and Paolo Ceravolo

On the Move to Meaningful Internet …, Jan 1, 2010

Abstract. This paper introduces the notion of Ontology Robustness in Evolution and discusses a so... more

ODDI: Ontology-Driven Data Integration

by Marcello Leida and Paolo Ceravolo

… and Engineering Systems, Jan 1, 2010

Data Integration systems are used to integrate heterogeneous data sources in a single view. Recen... more Data Integration systems are used to integrate heterogeneous data sources in a single view. Recent works on Business Intelligence do highlight the need of on-time, trustable and sound data access systems. This require for method based on a semi-automatic procedure that can provide reliable results. A crucial factor for any semi automatic algorithm is based on the matching operators implemented. Different categories of matching operators carry different semantics. For this reason combining them in a single algorithm is a non trivial process ...

A Toward Framework for Generic Uncertainty Management

by Marcello Leida and Paolo Ceravolo

Modeling Semantics of Business Rules

by Marcello Leida, Paolo Ceravolo, and Cristiano Fugazza

Proceedings of the Inaugural IEEE …

Using Ontologies to Map Concept Relations In a Data Integration System

by Marcello Leida and Paolo Ceravolo

Proceedings of the 2007 …, Jan 1, 2007

Knowledge Extraction from Semi-structured Data Based on Fuzzy Techniques

by Marco Viviani and Paolo Ceravolo

In this work we propose a fuzzy technique to compare XML documents belonging to a semi-structured... more In this work we propose a fuzzy technique to compare XML documents belonging to a semi-structured flow and sharing a common vocabulary of tags. Our approach is based on the idea of representing documents as fuzzy bags and, using a measure of comparison, evaluating structural similarities be-tween them. Then we suggest how to organize the extracted knowledge in a class hierarchy, choosing a technique related to the domain of interest, later to be converted into a user ontology.

Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata

by Marco Viviani and Paolo Ceravolo

IEEE Transactions on Knowledge and Data Engineering, 2007

We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts a... more We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run

OntoExtractor: A Fuzzy-Based Approach to Content and Structure-Based Metadata Extraction

by Marco Viviani and Paolo Ceravolo

This paper describes OntoExtractor a tool for extracting metadata from heterogeneous sources of i... more This paper describes OntoExtractor a tool for extracting metadata from heterogeneous sources of information, producing a “quick-and-dirty” hierarchy of knowledge. This tool is specifically tailored for a quick classification of semi-structured data. By this feature, OntoExtractor is convenient for dealing with a web-based data source.

Chapter 13 Bottom-up extraction and maintenance of ontology-based metadata

by Marco Viviani and Paolo Ceravolo

Capturing Intelligence, 2006

In this chapter, several flexible techniques aimed at extracting, maintaining and enriching seman... more In this chapter, several flexible techniques aimed at extracting, maintaining and enriching semantic-web style metadata are discussed. Such techniques were designed for being applied in the framework of dynamic Communities of Practice (CoP) interactions. Namely, we present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete objects. Unlike huge, “supply-side” normative ontologies,

Extending formal concept analysis by fuzzy bags

by Marco Viviani and Paolo Ceravolo

… , Les Cordeliers, France, july, 2-7, …, 2006

Extending formal concept analysis by fuzzy bags / P. Ceravolo, E. Damiani, M. Viviani - In: IPMU ... more

Emergent Semantics in Distributed Knowledge Management

by Marco Viviani, Tiziana Catarci, and Paolo Ceravolo

Organizations and enterprises have developed complex data and information exchange systems that a... more Organizations and enterprises have developed complex data and information exchange systems that are now vital for their daily operations. Currently available systems, however, face a major challenge. On todays global information infrastructure, data semantics is more and more context- and time-dependent, and cannot be fixed once and for all at design time. Identifying emerging relationships among previously unrelated information items (e.g., during data interchange) may dramatically increase their business value. This chapter introduce and discuss the notion of Emergent Semantics (ES), where both the representation of semantics and the discovery of the proper interpretation of symbols are seen as the result of a selforganizing process performed by distributed agents, exchanging symbols and adaptively developing the proper interpretation via multi-party cooperation and conflict resolution. Emergent data semantics is dynamically dependent on the collective behaviour of large communities of agents, which may have different and even conflicting interests and agendas. This is a research paradigm interpreting semantics from a pragmatic prospective. The chapter introduce this notion providing a discussion on the principles, research area and current state of the art.

Adding a Peer-to-Peer Trust Layer to Metadata Generators

by Marco Viviani and Paolo Ceravolo

In this paper we outline the architecture of a peer-to-peer Trust Layer that can be superimposed ... more In this paper we outline the architecture of a peer-to-peer Trust Layer that can be superimposed to metadata generators producing classifications, like our ClassBuilder and BTExact’s iPHI tools. Different techniques for aggregating trust values are also discussed. Our ongoing experimentation is aimed at validating the role of a Trust Layer as a non-intrusive, peer-to-peer technique for improving quality of automatically generated metadata.

Adding a Trust Layer to Semantic Web Metadata

by Marco Viviani and Paolo Ceravolo

We outline the architecture of a modular Trust Layer that can be superimposed to generic semantic... more We outline the architecture of a modular Trust Layer that can be superimposed to generic semantic Web-style metadata generation facilities. Also, we propose an experimental setting to generate and validate trust assertions on classification metadata generated by different tools (including our ClassBuilder) after a process of metadata standardization. Our experimentation is aimed at validating the role of our Trust Layer as a non-intrusive, user-centered quality improver for automatically generated metadata.

A Framework for Semi-Automatic Data Integration

by Marcello Leida and Paolo Ceravolo

Enterprise Information …, Jan 1, 2009

Recent studies on Business Intelligence highlights the need of on-time, trustable and sound data ... more Recent studies on Business Intelligence highlights the need of on-time, trustable and sound data access systems. Moreover the application of these systems in a flexible and dynamic environment requires for an approach based on automatic procedures that can provide reliable results. A crucial factor for any automatic data integration system is the matching process. Different categories of matching operators carry different semantics. For this reason combining them in a single algorithm is a non trivial process that have to take into account ...

OntoExtractor: a Fuzzy-Based Approach to Content and Structure-Based Metadata Extraction

by Marcello Leida and Paolo Ceravolo

On the Move to Meaningful …, Jan 1, 2006

An FCA-Based Mapping Generator

by Marcello Leida and Paolo Ceravolo

… and Factory Automation, …, Jan 1, 2008

We present an overview of ODDI an Ontology Driven Data Integration system based on Formal concept... more

Semantics-Aware Matching Strategy (SAMS) for the Ontology MeDiated Data Integration (ODDI)

by Marcello Leida and Paolo Ceravolo

International Journal of …, Jan 1, 2010

Data integration systems are used to integrate heterogeneous data sources in a single view. Recen... more Data integration systems are used to integrate heterogeneous data sources in a single view. Recent work on business intelligence highlights the need of on-time, reliable and sound data access systems relying on methods based on semi-automatic procedures. A crucial factor for any semi-automatic algorithm is that of the matching strategy. Different categories of matching operators carry different semantics. For this reason, combining them into a single strategy is a non-trivial process that has to take into account a variety of options. ...

ODDI: a Framework for Semi-Automatic Data Integration

by Marcello Leida and Paolo Ceravolo

ICEIS 2008: proceedings of the …, Jan 1, 2008

Which Role for An Ontology of Uncertainty?

by Marcello Leida and Paolo Ceravolo

Matching Operators In Data Integration

by Marcello Leida and Paolo Ceravolo

This paper discusses the role of matching operators in Data Integration process. The paper propos... more This paper discusses the role of matching operators in Data Integration process. The paper proposes acategorization of matching operators, with the aim of distinguishing different relations to the possiblegoals of an integration process and their impact on the implementation of the mappings defining thisintegration. In order to lead the discussion some example.

Ontology Robustness In Evolution

by Marcello Leida and Paolo Ceravolo

On the Move to Meaningful Internet …, Jan 1, 2010

Abstract. This paper introduces the notion of Ontology Robustness in Evolution and discusses a so... more

ODDI: Ontology-Driven Data Integration

by Marcello Leida and Paolo Ceravolo

… and Engineering Systems, Jan 1, 2010

Data Integration systems are used to integrate heterogeneous data sources in a single view. Recen... more Data Integration systems are used to integrate heterogeneous data sources in a single view. Recent works on Business Intelligence do highlight the need of on-time, trustable and sound data access systems. This require for method based on a semi-automatic procedure that can provide reliable results. A crucial factor for any semi automatic algorithm is based on the matching operators implemented. Different categories of matching operators carry different semantics. For this reason combining them in a single algorithm is a non trivial process ...

A Toward Framework for Generic Uncertainty Management

by Marcello Leida and Paolo Ceravolo

Modeling Semantics of Business Rules

by Marcello Leida, Paolo Ceravolo, and Cristiano Fugazza

Proceedings of the Inaugural IEEE …

Using Ontologies to Map Concept Relations In a Data Integration System

by Marcello Leida and Paolo Ceravolo

Proceedings of the 2007 …, Jan 1, 2007

Knowledge Extraction from Semi-structured Data Based on Fuzzy Techniques

by Marco Viviani and Paolo Ceravolo

In this work we propose a fuzzy technique to compare XML documents belonging to a semi-structured... more In this work we propose a fuzzy technique to compare XML documents belonging to a semi-structured flow and sharing a common vocabulary of tags. Our approach is based on the idea of representing documents as fuzzy bags and, using a measure of comparison, evaluating structural similarities be-tween them. Then we suggest how to organize the extracted knowledge in a class hierarchy, choosing a technique related to the domain of interest, later to be converted into a user ontology.

Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata

by Marco Viviani and Paolo Ceravolo

IEEE Transactions on Knowledge and Data Engineering, 2007

We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts a... more We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run

OntoExtractor: A Fuzzy-Based Approach to Content and Structure-Based Metadata Extraction

by Marco Viviani and Paolo Ceravolo

This paper describes OntoExtractor a tool for extracting metadata from heterogeneous sources of i... more This paper describes OntoExtractor a tool for extracting metadata from heterogeneous sources of information, producing a “quick-and-dirty” hierarchy of knowledge. This tool is specifically tailored for a quick classification of semi-structured data. By this feature, OntoExtractor is convenient for dealing with a web-based data source.

Chapter 13 Bottom-up extraction and maintenance of ontology-based metadata

by Marco Viviani and Paolo Ceravolo

Capturing Intelligence, 2006

In this chapter, several flexible techniques aimed at extracting, maintaining and enriching seman... more In this chapter, several flexible techniques aimed at extracting, maintaining and enriching semantic-web style metadata are discussed. Such techniques were designed for being applied in the framework of dynamic Communities of Practice (CoP) interactions. Namely, we present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete objects. Unlike huge, “supply-side” normative ontologies,

Extending formal concept analysis by fuzzy bags

by Marco Viviani and Paolo Ceravolo

… , Les Cordeliers, France, july, 2-7, …, 2006

Extending formal concept analysis by fuzzy bags / P. Ceravolo, E. Damiani, M. Viviani - In: IPMU ... more

Emergent Semantics in Distributed Knowledge Management

by Marco Viviani, Tiziana Catarci, and Paolo Ceravolo

Organizations and enterprises have developed complex data and information exchange systems that a... more Organizations and enterprises have developed complex data and information exchange systems that are now vital for their daily operations. Currently available systems, however, face a major challenge. On todays global information infrastructure, data semantics is more and more context- and time-dependent, and cannot be fixed once and for all at design time. Identifying emerging relationships among previously unrelated information items (e.g., during data interchange) may dramatically increase their business value. This chapter introduce and discuss the notion of Emergent Semantics (ES), where both the representation of semantics and the discovery of the proper interpretation of symbols are seen as the result of a selforganizing process performed by distributed agents, exchanging symbols and adaptively developing the proper interpretation via multi-party cooperation and conflict resolution. Emergent data semantics is dynamically dependent on the collective behaviour of large communities of agents, which may have different and even conflicting interests and agendas. This is a research paradigm interpreting semantics from a pragmatic prospective. The chapter introduce this notion providing a discussion on the principles, research area and current state of the art.

Adding a Peer-to-Peer Trust Layer to Metadata Generators

by Marco Viviani and Paolo Ceravolo

In this paper we outline the architecture of a peer-to-peer Trust Layer that can be superimposed ... more In this paper we outline the architecture of a peer-to-peer Trust Layer that can be superimposed to metadata generators producing classifications, like our ClassBuilder and BTExact’s iPHI tools. Different techniques for aggregating trust values are also discussed. Our ongoing experimentation is aimed at validating the role of a Trust Layer as a non-intrusive, peer-to-peer technique for improving quality of automatically generated metadata.

Adding a Trust Layer to Semantic Web Metadata

by Marco Viviani and Paolo Ceravolo

We outline the architecture of a modular Trust Layer that can be superimposed to generic semantic... more We outline the architecture of a modular Trust Layer that can be superimposed to generic semantic Web-style metadata generation facilities. Also, we propose an experimental setting to generate and validate trust assertions on classification metadata generated by different tools (including our ClassBuilder) after a process of metadata standardization. Our experimentation is aimed at validating the role of our Trust Layer as a non-intrusive, user-centered quality improver for automatically generated metadata.