Link prediction using a probabilistic description logic

Abstract Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology...
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Gespeichert in:

Autor*in:	Luna, José Eduardo Ochoa [verfasserIn] Revoredo, Kate Cozman, Fabio Gagliardi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Link prediction Probabilistic logic Description logics

Anmerkung:	© The Brazilian Computer Society 2013

Übergeordnetes Werk:	Enthalten in: Journal of the Brazilian Computer Society - London : Springer, 1996, 19(2013), 4 vom: 25. Apr., Seite 397-409
Übergeordnetes Werk:	volume:19 ; year:2013 ; number:4 ; day:25 ; month:04 ; pages:397-409

Links:	Volltext

DOI / URN:	10.1007/s13173-013-0108-8

Katalog-ID:	SPR030743613

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spelling	10.1007/s13173-013-0108-8 doi (DE-627)SPR030743613 (SPR)s13173-013-0108-8-e DE-627 ger DE-627 rakwb eng Luna, José Eduardo Ochoa verfasserin aut Link prediction using a probabilistic description logic 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Brazilian Computer Society 2013 Abstract Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable uncertainty in the task (the ontology is specified in the probabilistic description logic cr%$\mathcal{ALC }%$). The scalability of the approach is investigated, through a combination of semantic assumptions and graph-based features. We evaluate empirically our proposal, and compare it with standard solutions in the literature. Link prediction (dpeaa)DE-He213 Probabilistic logic (dpeaa)DE-He213 Description logics (dpeaa)DE-He213 Revoredo, Kate aut Cozman, Fabio Gagliardi aut Enthalten in Journal of the Brazilian Computer Society London : Springer, 1996 19(2013), 4 vom: 25. Apr., Seite 397-409 (DE-627)324613377 (DE-600)2028746-X 1678-4804 nnns volume:19 year:2013 number:4 day:25 month:04 pages:397-409 https://dx.doi.org/10.1007/s13173-013-0108-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2013 4 25 04 397-409
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allfieldsSound	10.1007/s13173-013-0108-8 doi (DE-627)SPR030743613 (SPR)s13173-013-0108-8-e DE-627 ger DE-627 rakwb eng Luna, José Eduardo Ochoa verfasserin aut Link prediction using a probabilistic description logic 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Brazilian Computer Society 2013 Abstract Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable uncertainty in the task (the ontology is specified in the probabilistic description logic cr%$\mathcal{ALC }%$). The scalability of the approach is investigated, through a combination of semantic assumptions and graph-based features. We evaluate empirically our proposal, and compare it with standard solutions in the literature. Link prediction (dpeaa)DE-He213 Probabilistic logic (dpeaa)DE-He213 Description logics (dpeaa)DE-He213 Revoredo, Kate aut Cozman, Fabio Gagliardi aut Enthalten in Journal of the Brazilian Computer Society London : Springer, 1996 19(2013), 4 vom: 25. Apr., Seite 397-409 (DE-627)324613377 (DE-600)2028746-X 1678-4804 nnns volume:19 year:2013 number:4 day:25 month:04 pages:397-409 https://dx.doi.org/10.1007/s13173-013-0108-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2013 4 25 04 397-409
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source	Enthalten in Journal of the Brazilian Computer Society 19(2013), 4 vom: 25. Apr., Seite 397-409 volume:19 year:2013 number:4 day:25 month:04 pages:397-409
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author	Luna, José Eduardo Ochoa
spellingShingle	Luna, José Eduardo Ochoa misc Link prediction misc Probabilistic logic misc Description logics Link prediction using a probabilistic description logic
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topic_title	Link prediction using a probabilistic description logic Link prediction (dpeaa)DE-He213 Probabilistic logic (dpeaa)DE-He213 Description logics (dpeaa)DE-He213
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title	Link prediction using a probabilistic description logic
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title_full	Link prediction using a probabilistic description logic
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author_browse	Luna, José Eduardo Ochoa Revoredo, Kate Cozman, Fabio Gagliardi
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author-letter	Luna, José Eduardo Ochoa
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title_sort	link prediction using a probabilistic description logic
title_auth	Link prediction using a probabilistic description logic
abstract	Abstract Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable uncertainty in the task (the ontology is specified in the probabilistic description logic cr%$\mathcal{ALC }%$). The scalability of the approach is investigated, through a combination of semantic assumptions and graph-based features. We evaluate empirically our proposal, and compare it with standard solutions in the literature. © The Brazilian Computer Society 2013
abstractGer	Abstract Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable uncertainty in the task (the ontology is specified in the probabilistic description logic cr%$\mathcal{ALC }%$). The scalability of the approach is investigated, through a combination of semantic assumptions and graph-based features. We evaluate empirically our proposal, and compare it with standard solutions in the literature. © The Brazilian Computer Society 2013
abstract_unstemmed	Abstract Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable uncertainty in the task (the ontology is specified in the probabilistic description logic cr%$\mathcal{ALC }%$). The scalability of the approach is investigated, through a combination of semantic assumptions and graph-based features. We evaluate empirically our proposal, and compare it with standard solutions in the literature. © The Brazilian Computer Society 2013
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title_short	Link prediction using a probabilistic description logic
url	https://dx.doi.org/10.1007/s13173-013-0108-8
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author2	Revoredo, Kate Cozman, Fabio Gagliardi
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up_date	2024-07-03T19:51:30.947Z
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