Ontology and the lexicon: a multidisciplinary perspective

Prévot, Laurent; Huang, Chu‐Ren; Calzolari, Nicoletta; Gangemi, Aldo; Lenci, Alessandro; Oltramari, Alessandro

doi:10.1017/cbo9780511676536.002

Cited by 21 publications

(17 citation statements)

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“…While ontologies formalize a shared conceptualization, ontology lexica [23] describe the lexical encoding of that conceptualization in a particular language. A number of models were proposed for describing the ontology-lexicon interface, among them lemon 7 (Lexicon Model for Ontologies) [18], which is currently subject to standardization efforts in a W3C community group 8 .…”

Section: The Lexicon-ontology Interfacementioning

confidence: 99%

ATOLL—A framework for the automatic induction of ontology lexica

Walter

Unger

Cimiano

2014

Data & Knowledge Engineering

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There is a range of large knowledge bases, such as Freebase and DBpedia, as well as linked data sets available on the web, but they typically lack lexical information stating how the properties and classes they comprise are realized lexically. Often only one label is attached, if at all, thus lacking rich linguistic information, e.g. about morphological forms, syntactic arguments or possible lexical variants and paraphrases. While ontology lexicon models like lemon allow for defining such linguistic information with respect to a given ontology, the cost involved in creating and maintaining such lexica is substantial, requiring a high manual effort. Towards lowering this effort we present ATOLL, a framework for the automatic induction of ontology lexica, based both on existing labels and dependency paths extracted from a text corpus. We instantiate ATOLL with respect to DBpedia as dataset and Wikipedia as corresponding corpus, and evaluate it by comparing the automatically generated lexicon with a manually constructed one. Our results clearly corroborate that our approach shows a high potential to be applied in a semi-automatic fashion in which a lexicon engineer can validate, reject or refine the automatically generated lexical entries, thus having a clear potential to contributing to the reduction the overall cost of creating ontology lexica.

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Section: The Lexicon-ontology Interfacementioning

confidence: 99%

ATOLL—A framework for the automatic induction of ontology lexica

Walter

Unger

Cimiano

2014

Data & Knowledge Engineering

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“…To sum up, we could say that lexical resources and ontologies tackle two different "semantic layers" and could be used in an integrated way to exploit their complementary strengths, as already pointed out in [9], that also mentions a distinction that is particularly meaningful to our purposes:…”

Section: Prospective Exploitation Of Lexical Resources and Ontologiesmentioning

confidence: 99%

“…On the other hand, ontologies, specially top-level ones, are not densely populated but offer a dense network of constraints for their concepts. [9,187] Being densely populated, WordNet promises to enhance coverage results, while ontologies, thanks to their being densely constrained, should make results more precise.…”

Section: Prospective Exploitation Of Lexical Resources and Ontologiesmentioning

confidence: 99%

Ontology-Assisted Object Detection: Towards the Automatic Learning with Internet

Setti

Cheng

Abdulhak

et al. 2013

Image Analysis and Processing – ICIAP 2013

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Abstract. Automatic detection approaches depend essentially on the use of classifiers, that in turn are based on the learning of a given training set. The choice of the training data is crucial: even if this aspect is often neglected, the visual information contained in the training samples can make the difference in a detection/classification scenario. A good training set has to be sufficiently informative to capture the nature of the object under analysis, but at the same time has to be generic enough to avoid overfitting and to cope with new instances of the object of interest. In this paper we follow those approaches that pursue automatic learning from Internet data. We try to show how such training set can be made more appropriate by leveraging on semantic technologies, like lexical resources and ontologies, in the task of retrieving images from the Web through the use of a search engine. Experiments on several object classes of the CalTech101 dataset promote our idea, showing an average increment on the detection accuracy of about 8%.

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“…A number of models have been proposed to represent such lexical knowledge, realizing what has been called the ontologylexicon interface [18], among them lemon 2 [12]. lemon is a model for the declarative specification of multilingual, machine-readable lexica in RDF that capture syntactic and semantic aspects of lexical items relative to some ontology.…”

Section: Introductionmentioning

confidence: 99%

M-ATOLL: A Framework for the Lexicalization of Ontologies in Multiple Languages

Walter

Unger

Cimiano

2014

The Semantic Web – ISWC 2014

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Abstract. Many tasks in which a system needs to mediate between natural language expressions and elements of a vocabulary in an ontology or dataset require knowledge about how the elements of the vocabulary (i.e. classes, properties, and individuals) are expressed in natural language. In a multilingual setting, such knowledge is needed for each of the supported languages. In this paper we present M-ATOLL, a framework for automatically inducing ontology lexica in multiple languages on the basis of a multilingual corpus. The framework exploits a set of language-specific dependency patterns which are formalized as SPARQL queries and run over a parsed corpus. We have instantiated the system for two languages: German and English. We evaluate it in terms of precision, recall and F-measure for English and German by comparing an automatically induced lexicon to manually constructed ontology lexica for DBpedia. In particular, we investigate the contribution of each single dependency pattern and perform an analysis of the impact of different parameters.

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Ontology and the lexicon: a multidisciplinary perspective

Cited by 21 publications

References 0 publications

ATOLL—A framework for the automatic induction of ontology lexica

ATOLL—A framework for the automatic induction of ontology lexica

Ontology-Assisted Object Detection: Towards the Automatic Learning with Internet

M-ATOLL: A Framework for the Lexicalization of Ontologies in Multiple Languages

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