Formal Model for Ontology Mapping Creation

Mocan, Adrian; Cimpian, Emilia; Kerrigan, Mick

doi:10.1007/11926078_33

Cited by 30 publications

(28 citation statements)

References 6 publications

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“…The step consists in defining some alignments between each local ontology network and the RO network. A description of the approach followed for designing these alignments or semantic mappings can be found in Mocan, Cimpian, andKerrigan (2006) and. Basically, the development of these mappings consists of four steps that must be carried out in an iterative manner, and these are the following: Every time a new ES wants to join the SEEMP marketplace, a new local ontology network should be built; additionally, it would be necessary to define the required alignments between this new local ontology network and the already existing RO network.…”

Section: Creating the Network Of Ontology Networkmentioning

confidence: 99%

A network of ontology networks for building e-employment advanced systems

Villazón-Terrazas

Ramírez

Suárez-Figueroa

et al. 2011

Expert Systems with Applications

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a b s t r a c tThis paper presents the development of a network of ontology networks that enables data mediation between the Employment Services (ESs) participating in a semantic interoperability platform for the exchange of Curricula Vitae (CVs) and job offers in different languages. Such network is formed by (1) a set of local ontology networks that are language dependent, in which each network represents the local and particular view that each ES has of the employment market; and (2) a reference ontology network developed in English that represents a standardized and agreed upon terminology of the European employment market. In this network each local ontology network is aligned with the reference ontology network so that search queries, CVs, and job offers can be mediated through these alignments from any ES. The development of the ontologies has followed the methodological guidelines issued by the NeOn Methodology and is focused mainly on scenarios that involve reusing and re-engineering knowledge resources already agreed upon by employment experts and standardization bodies. This paper explains how these methodological guidelines have been applied for building e-employment ontologies. In addition, it shows that the approach to building ontologies by reusing and re-engineering agreed upon non-ontological resources speeds the ontology development, reduces development costs, and retrieves knowledge already agreed upon by a community of people in a more formal representation.

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Section: Creating the Network Of Ontology Networkmentioning

confidence: 99%

A network of ontology networks for building e-employment advanced systems

Villazón-Terrazas

Ramírez

Suárez-Figueroa

et al. 2011

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…The work in [27] proposed a graphical visualization of alignments based on cognitive studies. In turn, the work in [60,61] has provided an environment for manually designing complex alignments through the use of connected perspective that allows to quickly deemphasize non relevant aspects of the ontologies being matched while keeping the connections between relevant entities. This line of work must be still consolidated and it should be possible to seamlessly plug the results obtained here into an alignment management system (see §13).…”

Section: User Involvementmentioning

confidence: 99%

Ten Challenges for Ontology Matching

Shvaiko¹,

Euzenat

2008

Lecture Notes in Computer Science

210

135

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Abstract. This paper aims at analyzing the key trends and challenges of the ontology matching field. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which is robust enough to be the basis for future development, and which is usable by non expert users. In this paper we first provide the basics of ontology matching with the help of examples. Then, we present general trends of the field and discuss ten challenges for ontology matching, thereby aiming to direct research into the critical path and to facilitate progress of the field.

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“…In research, data alignment tools have been built mostly for experts and research has focused primarily on data modeling theories and automated agents for ontology alignment [7,8,10,11,12,13,14,16] rather than on user interfaces for making practical use of aggregated data. Because they specialize only in data alignment, they implicitly assume that users work with the data in delineated stages, first aligning the data and cleaning it up, and then making use of that data in some other tools.…”

Section: Related Workmentioning

confidence: 99%

Potluck: Data mash-up tool for casual users

Huynh

Miller

Karger

2008

Journal of Web Semantics

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Abstract. As more and more reusable structured data appears on the Web, casual users will want to take into their own hands the task of mashing up data rather than wait for mash-up sites to be built that address exactly their individually unique needs. In this paper, we present Potluck, a Web user interface that lets casual users -those without programming skills and data modeling expertise-mash up data themselves.Potluck is novel in its use of drag and drop for merging fields, its integration and extension of the faceted browsing paradigm for focusing on subsets of data to align, and its application of simultaneous editing for cleaning up data syntactically. Potluck also lets the user construct rich visualizations of data in-place as the user aligns and cleans up the data. This iterative process of integrating the data while constructing useful visualizations is desirable when the user is unfamiliar with the data at the beginning-a common case-and wishes to get immediate value out of the data without having to spend the overhead of completely and perfectly integrating the data first.A user study on Potluck indicated that it was usable and learnable, and elicited excitement from programmers who, even with their programming skills, previously had great difficulties performing data integration.

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Formal Model for Ontology Mapping Creation

Cited by 30 publications

References 6 publications

A network of ontology networks for building e-employment advanced systems

A network of ontology networks for building e-employment advanced systems

Ten Challenges for Ontology Matching

Potluck: Data mash-up tool for casual users

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