References of "Nogueras-Iso, Javier"
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See detailDesign and evaluation of a semantic enrichment process for bibliographic databases
Lacasta, Javier; Nogueras-Iso, Javier; Falquet, Gilles et al

in Data & Knowledge Engineering (2013), 88

The limited semantics of thesauri and similar knowledge models hinder the searching and browsing possibilities of the bibliographic databases classified with this type of resources. This work proposes an ... [more ▼]

The limited semantics of thesauri and similar knowledge models hinder the searching and browsing possibilities of the bibliographic databases classified with this type of resources. This work proposes an automatic process to convert a knowledge model into a domain ontology through the alignment with DOLCE, an upper level ontology. This process is facilitated by an intermediary alignment with Wordnet, a lexical model. The process has been tested with the thesauri and bibliographic databases of Urbamet and the European Urban Knowledge Network. The Urbamet model has been used to create an atlas of urban related resources with advanced search capabilities. [less ▲]

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See detailTransformation of a keyword indexed collection into a semantic repository: applicability to the urban domain
Lacasta, Javier; Nogueras-Iso, Javier; Teller, Jacques ULg et al

in Proceedings of the 15th international conference on Theory and practice of digital libraries: research and advanced technology for digital libraries (2011)

In the information retrieval context, resource collections are frequently classified using thesauri. However, the limited semantics pro- vided by thesauri restricts the collection search and browsing ... [more ▼]

In the information retrieval context, resource collections are frequently classified using thesauri. However, the limited semantics pro- vided by thesauri restricts the collection search and browsing capabilities. This work focuses on improving these capabilities by transforming a set of resources indexed according to a thesaurus into a semantically tagged collection. The core mechanism for building this collection is based on the conversion of the domain specific thesaurus (indexing the collection of resources) into a domain ontology connected to an upper level ontol- ogy. The feasibility of this work has been tested in the urban domain by transforming the resources accessible through the European Urban Knowledge Network into a Linked Data repository. [less ▲]

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See detailTransformation of Urban Knowledge Sources to Ontologies
Nogueras-Iso, Javier; Lacasta, Javier; Teller, Jacques ULg et al

in Falquet, Gilles; Métral, Claudine; Teller, Jacques (Eds.) et al Ontologies in Urban Development Projects (2011)

Since the development of ontologies from scratch requires much time and many resources, the activity of knowledge acquisition constitutes one of the most impor- tant steps at the beginning of the ontology ... [more ▼]

Since the development of ontologies from scratch requires much time and many resources, the activity of knowledge acquisition constitutes one of the most impor- tant steps at the beginning of the ontology development process. This activity is essential in all the different methodologies for ontology design as a previous step to the conceptualization and formalization phases. And as its name indicates, this activity is devoted to gather all available knowledge resources describing the domain of the ontology and identify the most important terms in the domain. This chapter is focused on the study of methods and techniques for the (semi-) automatic processing of knowledge resources that may alleviate the work of knowledge acquisition. This task is known as ontology learning in the literature of ontological engineering. The aim of ontology learning is to apply the most appropriate methods to transform unstructured (e.g., text corpora), semi-structured (e.g., folksonomies, HTML pages) and structured data sources (e.g., databases, thesauri) into conceptual structures. The methods of ontology learning are usually connected with the activity of ontology population, which also relies on (semi-)automatic methods to transform unstruc- tured, semi-structured and structured data sources into instance data (i.e., instances of ontology concepts). [less ▲]

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See detailOntologies in the Geographic Information Sector
Billen, Roland ULg; Nogueras-Iso, Javier; López-Pellicer, F. Javier et al

in Falquet, Gilles; Métral, Claudine; Teller, Jacques (Eds.) et al Ontologies in Urban Development Projects (2011)

Geographical information (GI) or geoinformation describes phenomena associated directly or indirectly with a location (coordinates systems, address systems…) with respect to the Earth’s surface. Such ... [more ▼]

Geographical information (GI) or geoinformation describes phenomena associated directly or indirectly with a location (coordinates systems, address systems…) with respect to the Earth’s surface. Such phenomena can be either spatially discrete (represented by geometric primitives like points, lines, regions, etc.) such as a municipality, a road axis, etc. or spatially continuous (represented by interpolation on an image grid for example) such as terrain’s elevation, pollution diffusion, etc. GI is created by manipulating geographic data (or geospatial data) in a computerized system. Geospatial data can be acquired by different means: topographic survey, remote sensing, aerial photographs, GPS, laserscan, and all other types of sensors or survey techniques. Traditionally, these data are the core component of Geographic Information Systems (GIS), which is the term commonly used to refer to the software packages that allow to capture, store, check, integrate, manipulate, analyze and display them. [less ▲]

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See detailOntology Learning from Thesauri: An Experience in the Urban Domain
Nogueras-Iso, Javier; Lacasta, Javier; Teller, Jacques ULg et al

in Gargouri, Faiez; Jaziri, Wassim (Eds.) Ontology Theory, Management and Design: Advanced Tools and Models (2010)

Ontology learning is the term used to encompass methods and techniques employed for the (semi-) automatic processing of knowledge resources that facilitate the acquisition of knowledge during ontology ... [more ▼]

Ontology learning is the term used to encompass methods and techniques employed for the (semi-) automatic processing of knowledge resources that facilitate the acquisition of knowledge during ontology construction. This chapter focuses on ontology learning techniques using thesauri as input sources. Thesauri are one of the most promising sources for the creation of domain ontologies thanks to the richness of term definitions, the existence of a priori relationships between terms, and the consensus provided by their extensive use in the library context. Apart from reviewing the state of the art, this chapter shows how ontology learning techniques can be applied in the urban domain for the development of domain ontologies. [less ▲]

Detailed reference viewed: 57 (5 ULg)