Global Schema Generation Using Formal Ontologies

Hakimpour, Farshad; Geppert, Andreas

doi:10.1007/3-540-45816-6_31

Cited by 33 publications

(18 citation statements)

References 6 publications

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“…Requirement analysis, which is based on the i* framework to represent user requirements; 2. Source integration, which produces an integrated schema by reconciling data sources using an ontological approach [27,28,29,30]. 3.…”

Section: Modelsmentioning

confidence: 99%

A Proposal of Methodology for Designing Big Data Warehouses

Tria¹,

Lefons²,

Tangorra³

2018

Preprint

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Big Data warehouses are a new class of databases that largely use unstructured and volatile data for analytical purpose. Examples of this kind of data sources are those coming from the Web, such as social networks and blogs, or from sensor networks, where huge amounts of data may be available only for short intervals of time. In order to manage massive data sources, a strategy must be adopted to define multidimensional schemas in presence of fast-changing situations or even undefined business requirements. In the paper, we propose a design methodology that adopts agile and automatic approaches, in order to reduce the time necessary to integrate new data sources and to include new business requirements on the fly. The data are immediately available for analyses, since the underlying architecture is based on a virtual data warehouse that does not require the importing phase. Examples of application of the methodology are presented along the paper in order to show the validity of this approach compared to a traditional one.

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Section: Modelsmentioning

confidence: 99%

A Proposal of Methodology for Designing Big Data Warehouses

Tria¹,

Lefons²,

Tangorra³

2018

Preprint

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“…COMA++ (Aumueller et al 2005) supports multiple matchers and also uses a taxonomy that acts as an intermediate ontology for schema or ontology matching. (Hakimpour et al 2002) merges different ontologies into a single global schema, using similarity relations. (Embley et al 2004) uses a combination of structural similarity between two schemas and also a domain-specific ontology to discover mappings.…”

Section: Related Workmentioning

confidence: 99%

Configurable meta-search in the job domain

Naz

Dorn

Poulovassilis

2010

IJWET

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To aid job seekers searching for job vacancies, we have developed a new configurable meta-search engine for the human resources domain. In this paper we describe the three main components of our meta-search engine -the query interface generator, query dispatcher and information extractor -which collectively support the meta-search engine creation and usage. One of the important challenges in accessing heterogeneous and distributed data via a meta-search engine is schema/data matching and integration. We describe an approach to schema and data integration for meta-search engines that helps to resolve the semantic heterogeneities between different source search engines. Our approach is a hybrid one, in that we use multiple matching criteria and multiple matchers. A domain-specific ontology serves as a global ontology and allows us to resolve semantic heterogeneities by deriving mappings between the source search engine interfaces and the ontology. The mappings are used to generate an integrated meta-search query interface, to support query processing within the meta-search engine, and to resolve semantic conflicts arising during result extraction from the source search engines. Experiments conducted in the job search domain show that our hybrid approach increases the correctness of matching during the automatic integration of source search interfaces. Our system aims to support job meta-search providers in the rapid development of meta-search engines. Our use of a domain ontology and multiple matchers T. Naz, J.Dorn, A.Poulovassilis helps in the semantic understanding of job descriptions and provides a job seeker with integrated access to jobs from a variety of Websites.Keywords: Meta-search engine, schema matching and integration, information extraction and integration, ontology based schema integration, job search.

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“…In other proposals, such as those by Magnani et al [14] and Hakimpour [7] et al, correspondences are defined as extentional constraints between the elements of two schemas. For example, correspondences of this form can be used to specify that the extent of a relation is covered by the extent of another relation.…”

Section: Related Workmentioning

confidence: 99%

“…For example, correspondences can be used to relate one element of a given schema to one element of another schema, e.g., Sint .Staff , S1 .Employee , or to relate multiple elements of one schema to one element of another schema, e.g., the correspondence Sint .Staff , {S1 .Employee, S1 .Department} states that the relation Staff in the integration schema is semantically equivalent to some combination of the relations Employee and Department of the source schema S 1 . There are several models for drawing schematic correspondences in the literature [17,7,14,12], of which the model proposed by Kim et al [12] is perhaps the most comprehensive. In the second phase of mapping specification, the views that implement the mappings necessary for rewriting the queries issued against the integration schema into queries over the schemas of the sources are specified.…”

Section: Introductionmentioning

confidence: 99%

Defining and Using Schematic Correspondences for Automatically Generating Schema Mappings

Belhajjame

Paton

et al. 2009

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Abstract. Mapping specification has been recognised as a critical bottleneck to the large scale deployment of data integration systems. A mapping is a description using which data structured under one schema are transformed into data structured under a different schema, and is central to data integration and data exchange systems. In this paper, we argue that the classical approach of correspondence identification followed by (manual) mapping generation can be simplified through the removal of the second step by judicious refinement of the correspondences captured. As a step in this direction, we present in this paper a model for schematic correspondences that builds on and extends the classification proposed by Kim et al. to cater for the automatic derivation of mappings, and present an algorithm that shows how correspondences specified in the model proposed can be used for deriving schema mappings. The approach is illustrated using a case study from integration in proteomics.

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Global Schema Generation Using Formal Ontologies

Cited by 33 publications

References 6 publications

A Proposal of Methodology for Designing Big Data Warehouses

A Proposal of Methodology for Designing Big Data Warehouses

Configurable meta-search in the job domain

Defining and Using Schematic Correspondences for Automatically Generating Schema Mappings

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