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ISEP-IPP, School of Engineering of Polytechnic of Porto, GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development and

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3 Papers

An Ontology-based Methodology for Reusing Data Cleaning Knowledge

Topics: Knowledge Representation; Expert Systems; Integration and Interoperability; Knowledge Engineering

Authors: Ricardo Almeida ¹ , Paulo Maio ² , Paulo Oliveira ² and João Barroso ³

Affiliations: ¹ ISEP-IPP and School of Engineering of Polytechnic of Porto, Portugal , ² ISEP-IPP, School of Engineering of Polytechnic of Porto, GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development and, Portugal , ³ UTAD – University of Trás-os-Montes and Alto Douro, Portugal

Digital Object Identifier: 10.5220/0005596402020211

Proceedings of KEOD 2015/IC3K, ISBN: 978-989-758-158-8

Quick Abstract

The organizations’ demand to integrate several heterogeneous data sources and an ever-increasing volume of data is revealing the presence of quality problems in data. Currently, most of the data cleaning approaches (for detection and correction of data quality problems) are tailored for data sources with the same schema and sharing the same data model (e.g., relational model). On the other hand, these approaches are highly dependent on a domain expert to specify the data cleaning operations. This paper extends a previously proposed data cleaning methodology that reuses cleaning knowledge specified for other data sources. The methodology is further detailed/refined by specifying the requirements that a data cleaning operations vocabulary must satisfy. Ontologies in RDF/OWL are proposed as the data model for an abstract representation of the data schemas, no matter which data model is used (e.g., relational; graph). Existing approaches, methods and techniques that support the implementation of the proposed methodology, in general, and specifically of the data cleaning operations vocabulary are also presented and discussed in this paper.

Related Ontology Subjects/Areas/Topics (8)

Artificial Intelligence	Health Information Systems	Knowledge-Based Systems
Biomedical Engineering	Knowledge Engineering and Ontology Development	Symbolic Systems
Expert Systems	Knowledge Representation

I3OM – An Iterative, Incremental and Interactive Approach for Ontology Navigation based on Ontology Modularization

Topics: Ontology Engineering; Knowledge Representation; Human-Machine Cooperation; Applications and Case-Studies

Authors: Ricardo Brandão , Paulo Maio and Nuno Silva

Affiliation: Polytechnic of Porto, Portugal

Digital Object Identifier: 10.5220/0004145602650270

Proceedings of KEOD 2012/IC3K, ISBN: 978-989-8565-30-3

Quick Abstract

Although ontologies are used to describe a specific domain of interest, they can grow in size exponentially, compromising their usage. Furthermore, current ontology engineering tools do not effectively support the data/information visualization and navigation described through large ontologies. To address these issues, we claim that the experience and results of navigating/browsing ontology-described data can profit from the modularization of the ontologies underlying the repositories. For that, we propose the I3OM process that facilitates ontology-oriented navigation and contextualized information retrieval by combining different ontology modularization techniques into an iterative, incremental and interactive process.

Related Ontology Subjects/Areas/Topics (12)

Applications and Case-studies	Human-Machine Cooperation	Knowledge-Based Systems
Artificial Intelligence	Information Systems Analysis and Specification	Ontologies and the Semantic Web
Data Engineering	Knowledge Engineering and Ontology Development	Ontology Engineering
Enterprise Information Systems	Knowledge Representation	Symbolic Systems

GOALS – A TEST-BED FOR ONTOLOGY MATCHING

Topics: Applications and Case-Studies; Ontology Matching and Alignment

Authors: Paulo Maio and Nuno Silva

Affiliation: School of Engineering, Polytechnic of Porto, Portugal

Digital Object Identifier: 10.5220/0002309202930299

Proceedings of KEOD 2009/IC3K, ISBN: 978-989-674-012-2

Quick Abstract

State-of-the-art ontology matching systems rely on the combination of basic matching techniques but good results are only achieved when processing particular classes of ontologies. Furthermore, they are quite restrictive with respect to their internal configuration, as they are committed to a pre-defined architecture and workflow. Additionally, the skilful selection of matchers and the respective combination and configuration process is difficult and time consuming. Additionally it is hard to test and evaluate. This paper presents a test-bed system that eases the creation of new matching systems. It promotes the reusability, the combination and the configuration of existing matchers, encouraging the development of new matching algorithms able to fill specific open matching gaps exploiting existing methods and algorithms.

Related Ontology Subjects/Areas/Topics (6)

Applications and Case-studies		Knowledge Engineering and Ontology Development		Ontology Matching and Alignment
Artificial Intelligence		Knowledge-Based Systems		Symbolic Systems