LexInfo: A declarative model for the lexicon-ontology interface

Challenges for the multilingual Web of Data

This paper describes a scenario-based methodology called the NeOn Methodology framework. The aim of this framework is to speed up the construction of ontologies and ontology networks by reusing available knowledge resources (ontologies, non-ontological resources and ontology design patterns). The methodology is founded on four pillars: (1) a glossary of processes and activities; (2) a set of nine scenarios for building ontologies and ontology networks; (3) two ontology life-cycle models; and (4) a set of prescriptive methodological guidelines for performing specific processes and activities. The paper also shows how to apply this methodology framework to develop an ontology network within the multimedia domain. Finally, it presents an empirical evaluation of the NeOn Methodology framework.

The NeOn Methodology framework: A scenario-based methodology for ontology development

Ontologies are at the heart of knowledge management and make use of information that is not only written in English but also in many other natural languages. In order to enable knowledge discovery, sharing and reuse of these multilingual ontologies, it is necessary to support ontology mapping despite natural language barriers. This paper examines the soundness of a generic approach that involves machine translation tools and monolingual ontology matching techniques in cross-lingual ontology mapping scenarios. In particular, experimental results collected from case studies which engage mappings of independent ontologies that are labeled in English and Chinese are presented. Based on findings derived from these studies, limitations of this generic approach are discussed. It is shown with evidence that appropriate translations of conceptual labels in ontologies are of crucial importance when applying monolingual matching techniques in cross-lingual ontology mapping. Finally, to address the identified challenges, a semantic-oriented cross-lingual ontology mapping (SOCOM) framework is proposed and discussed.

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Cross-Lingual Ontology Mapping --- An Investigation of the Impact of Machine Translation

Organizations working in amultilingual environment demand multilingual ontologies. To solve this problem we propose LabelTranslator, a system that automatically localizes ontologies. Ontology localization consists of adapting an ontology to a concrete language and cultural community.

LabelTranslator takes as input an ontology whose labels are described in a source natural language and obtains the most probable translation into a target natural language of each ontology label. Our main contribution is the automatization of this process which reduces human efforts to localize an ontology manually. First, our system uses a translation service which obtains automatic translations of each ontology label (name of an ontology term) from/into English, German, or Spanish by consulting different linguistic resources such as lexical databases, bilingual dictionaries, and terminologies. Second, a ranking method is used to sort each ontology label according to similarity with its lexical and semantic context.

The experiments performed in order to evaluate the quality of translation show that our approach is a good approximation to automatically enrich an ontology with multilingual information.

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Enriching an ontology with multilingual information

International organizations (e.g., FAO, WHO, etc.) are increasingly expressing the need for multilingual ontologies for different purposes, e.g., ontology-based multilingual machine translation, multilingual information retrieval. However, most of the ontologies built so far have mainly English or another natural language as basis. Since multilingual ontology building is a very expensive and time-consuming undertaking, we propose methods for guiding users in the localization of ontologies, and provide tools for supporting the process. The main contributions of this paper are: i) the description of a generic Ontology Localization Activity and a methodology for guiding in the localization of ontologies; ii) the description of a tool built according to the guidelines proposed for an automatic localization of ontologies; and iii) a set of experiments used to evaluate the methodological and technological aspects of the Ontology Localization Activity.

Ontology localization

This demo proposal aims at providing support for the localization of ontologies, and as a result at obtaining multilingual ontologies. We briefly present an advanced version of LabelTranslator, our system to localize ontology terms in different natural languages. The current version of the system differs from previous works reported in [1,2] in that it relies on a modular approach to store the linguistic information associated to ontology terms. Additionally, it uses a synchronization method to maintain the conceptual and linguistic information updated.

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Multilingual and Localization Support for Ontologies

Ontologies and taxonomies are widely used to organize concepts providing the basis for activities such as indexing, and as background knowledge for NLP tasks. As such, translation of these resources would prove useful to adapt these systems to new languages. However, we show that the nature of these resources is significantly different from the "free-text" paradigm used to train most statistical machine translation systems. In particular, we see significant differences in the linguistic nature of these resources and such resources have rich additional semantics. We demonstrate that as a result of these linguistic differences, standard SMT methods, in particular evaluation metrics, can produce poor performance. We then look to the task of leveraging these semantics for translation, which we approach in three ways: by adapting the translation system to the domain of the resource; by examining if semantics can help to predict the syntactic structure used in translation; and by evaluating if we can use existing translated taxonomies to disambiguate translations. We present some early results from these experiments, which shed light on the degree of success we may have with each approach.

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Combining statistical and semantic approaches to the translation of ontologies and taxonomies

This demo proposal briefly presents LabelTranslator, a system that suggests translations of ontology labels, with the purpose of localizing ontologies LabelTranslator takes as input an ontology whose labels are described in a source natural language and obtains the most probable translation of each ontology label into a target natural language Our main contribution is the automatization of this process, which reduces human efforts to localize manually the ontology

/pdf/labeltranslator-a-tool-to-automatically-localize-an-ontology-2ox8uxb3sl.pdf

Mauricio Espinoza

Papers

Enriching an ontology with multilingual information

Ontology localization

Multilingual and Localization Support for Ontologies

Combining statistical and semantic approaches to the translation of ontologies and taxonomies

LabelTranslator - a tool to automatically localize an ontology