A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.

In a recent paper, we introduce a new family of Information Content (IC) models based on the estimation of the conditional probability between child and parent concepts. This work is encouraged by the nding of two drawbacks in the computational method of our aforementioned family of IC models, as we...

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Detalhes bibliográficos
Autores: Lastra-Díaz, Juan J., García Serrano, Ana Mª
Formato: informe técnico
Fecha de publicación:2024
País:España
Recursos:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/9886
Acesso em linha:https://hdl.handle.net/20.500.14468/9886
Access Level:acceso abierto
Palavra-chave:Intrinsic Information Content models
ontology-based semantic similarity measures
IC- based similarity measures
word similarity benchmark
semantic similarity
concept similarity model
experimental survey
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spelling A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.Lastra-Díaz, Juan J.García Serrano, Ana MªIntrinsic Information Content modelsontology-based semantic similarity measuresIC- based similarity measuresword similarity benchmarksemantic similarityconcept similarity modelexperimental surveyIn a recent paper, we introduce a new family of Information Content (IC) models based on the estimation of the conditional probability between child and parent concepts. This work is encouraged by the nding of two drawbacks in the computational method of our aforementioned family of IC models, as well as other two gaps in the literature. First gap is that two of our cognitive IC models do not satisfy the axiom that constrains the sum of probabilities on the leaf nodes to be 1, whilst some ontologies with multiple inheritance could prevent the IC model satisfying the growing monotonicity axiom in concepts with multiple parents. Second gap is the lack of a complete and updated experimental survey including a pairwise statistical signi cance analysis between most IC models and ontology-based similarity measures. Finally a third gap is the lack of replication and con rmation of previous methods and results in most works. The latest two gaps are especially signi cant in the current state of the problem, in which there is no convincing winner within the family of intrinsic IC-based similarity measures and the performance margin is very narrow. In order to bridge the aforementioned gaps, this paper introduces the following contributions: (1) a re nement of our recent family of well-founded Information Content (IC) models; (2) eight new intrinsic IC models and one new corpus-based IC model; and (3) a very detailed experimental survey of ontology-based similarity measures and Information Content (IC) models on WordNet, including the evaluation and statistical signi cance analysis on the ve most signi cant datasets of most ontology-based similarity measures and all WordNet-based IC models reported in the literature, with the only exception of the IC models recently introduced by Harispe et al. (2015a) and Ben Aouicha et al. (2016b). The evaluation is entirely based on a Java software library called HESML which has been developed by the authors in order to replicate all methods evaluated herein. The new IC models obtain rivaling results as regard the state-of-the-art methods and improve our previous mod- els, whilst the experimental survey allows a detailed and conclusive image of the state of the problem to be drawn by setting the new state of the art and quantifying the main achievements of the last three decades.Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticose-Spacio UNED20242024-05-20reporthttp://purl.org/coar/resource_type/c_93fcinfo:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/20.500.14468/9886reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:e-spacio.uned.es:20.500.14468/98862026-06-06T12:38:31Z
dc.title.none.fl_str_mv A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.
title A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.
spellingShingle A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.
Lastra-Díaz, Juan J.
Intrinsic Information Content models
ontology-based semantic similarity measures
IC- based similarity measures
word similarity benchmark
semantic similarity
concept similarity model
experimental survey
title_short A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.
title_full A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.
title_fullStr A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.
title_full_unstemmed A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.
title_sort A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.
dc.creator.none.fl_str_mv Lastra-Díaz, Juan J.
García Serrano, Ana Mª
author Lastra-Díaz, Juan J.
author_facet Lastra-Díaz, Juan J.
García Serrano, Ana Mª
author_role author
author2 García Serrano, Ana Mª
author2_role author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv Intrinsic Information Content models
ontology-based semantic similarity measures
IC- based similarity measures
word similarity benchmark
semantic similarity
concept similarity model
experimental survey
topic Intrinsic Information Content models
ontology-based semantic similarity measures
IC- based similarity measures
word similarity benchmark
semantic similarity
concept similarity model
experimental survey
description In a recent paper, we introduce a new family of Information Content (IC) models based on the estimation of the conditional probability between child and parent concepts. This work is encouraged by the nding of two drawbacks in the computational method of our aforementioned family of IC models, as well as other two gaps in the literature. First gap is that two of our cognitive IC models do not satisfy the axiom that constrains the sum of probabilities on the leaf nodes to be 1, whilst some ontologies with multiple inheritance could prevent the IC model satisfying the growing monotonicity axiom in concepts with multiple parents. Second gap is the lack of a complete and updated experimental survey including a pairwise statistical signi cance analysis between most IC models and ontology-based similarity measures. Finally a third gap is the lack of replication and con rmation of previous methods and results in most works. The latest two gaps are especially signi cant in the current state of the problem, in which there is no convincing winner within the family of intrinsic IC-based similarity measures and the performance margin is very narrow. In order to bridge the aforementioned gaps, this paper introduces the following contributions: (1) a re nement of our recent family of well-founded Information Content (IC) models; (2) eight new intrinsic IC models and one new corpus-based IC model; and (3) a very detailed experimental survey of ontology-based similarity measures and Information Content (IC) models on WordNet, including the evaluation and statistical signi cance analysis on the ve most signi cant datasets of most ontology-based similarity measures and all WordNet-based IC models reported in the literature, with the only exception of the IC models recently introduced by Harispe et al. (2015a) and Ben Aouicha et al. (2016b). The evaluation is entirely based on a Java software library called HESML which has been developed by the authors in order to replicate all methods evaluated herein. The new IC models obtain rivaling results as regard the state-of-the-art methods and improve our previous mod- els, whilst the experimental survey allows a detailed and conclusive image of the state of the problem to be drawn by setting the new state of the art and quantifying the main achievements of the last three decades.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-05-20
dc.type.none.fl_str_mv report
http://purl.org/coar/resource_type/c_93fc
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/9886
url https://hdl.handle.net/20.500.14468/9886
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos
publisher.none.fl_str_mv Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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