Towards semantic association rules mining from ontology-based semantic trajectories
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFSCAR |
Texto Completo: | https://repositorio.ufscar.br/handle/ufscar/14554 |
Resumo: | Different technologies and social-cultural aspects of our lives have allowed the acquisition of people's mobility data. The same applies to other moving objects, such as birds with GPS trackers and hurricanes with real-time satellite data. Although these raw positioning and timings are useful in many applications, it has been long recognized by the Trajectory Data community that semantics are required to capture the complexity of humans' and other objects' behaviors. Semantic Trajectories were proposed in this context as raw trajectories enriched with semantic annotations and possibly interlinked with external data. Based on these requirements, many works incorporate concepts and technologies from the Semantic Web to deal with the complexity of merging, representing, and querying heterogeneous data. They usually use ontologies to represent and manipulate concepts such as Moving Objects, Trajectories, Stops and Moves, and semantic aspects related to each of them. Nonetheless, we find that no previous work has explored mining patterns from these ontology-based representations. On the contrary, current efforts use standard association rule mining algorithms, such as Apriori, which require propositional data represented as Boolean feature vectors. To mine patterns aware of the semantic relations in a Semantic Trajectory ontology, we explore algorithms from the Knowledge Base Refinement field. These methods were proposed to use real-world facts represented in Knowledge Bases such as YAGO and DBPedia to infer new entities and relationships. We build on previous works describing ontology-based trajectory representations and tackle the knowledge discovery task using AMIE, a well-known state-of-the-art KB rule mining algorithm. This approach mines patterns in the form of Horn rules, which allows us to investigate associations between time, spatial, and semantic relations interlinking trajectory events. We show that representations previously proposed in the Semantic Trajectory community are not suitable to be directly mined by this approach. However, they can be easily extended to power the AMIE algorithm. We also describe and address different issues that arise when using a domain-agnostic mining algorithm. The proposed data pipeline mines interesting patterns in experiments using Foursquare datasets. Nonetheless, there is a large number of rules which state facts that are too general. We build on these issues and argue in favor of the design of a domain-specific mining algorithm. We discuss future opportunities based on the acquired experience and experiments. Our approach shows how the Semantic Trajectory and Knowledge Base Refinement communities have built in recent years a large number of representations and mining approaches that could be put together to mine rules with rich semantic expressiveness from semantic data. |
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Petri, Antonio Carlos FalcãoSilva, Diego Furtadohttp://lattes.cnpq.br/7662777934692986http://lattes.cnpq.br/3663434860348677a61505e8-e235-4ccd-b98c-08fe0cee428d2021-07-08T11:46:10Z2021-07-08T11:46:10Z2021-02-09PETRI, Antonio Carlos Falcão. Towards semantic association rules mining from ontology-based semantic trajectories. 2021. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2021. Disponível em: https://repositorio.ufscar.br/handle/ufscar/14554.https://repositorio.ufscar.br/handle/ufscar/14554Different technologies and social-cultural aspects of our lives have allowed the acquisition of people's mobility data. The same applies to other moving objects, such as birds with GPS trackers and hurricanes with real-time satellite data. Although these raw positioning and timings are useful in many applications, it has been long recognized by the Trajectory Data community that semantics are required to capture the complexity of humans' and other objects' behaviors. Semantic Trajectories were proposed in this context as raw trajectories enriched with semantic annotations and possibly interlinked with external data. Based on these requirements, many works incorporate concepts and technologies from the Semantic Web to deal with the complexity of merging, representing, and querying heterogeneous data. They usually use ontologies to represent and manipulate concepts such as Moving Objects, Trajectories, Stops and Moves, and semantic aspects related to each of them. Nonetheless, we find that no previous work has explored mining patterns from these ontology-based representations. On the contrary, current efforts use standard association rule mining algorithms, such as Apriori, which require propositional data represented as Boolean feature vectors. To mine patterns aware of the semantic relations in a Semantic Trajectory ontology, we explore algorithms from the Knowledge Base Refinement field. These methods were proposed to use real-world facts represented in Knowledge Bases such as YAGO and DBPedia to infer new entities and relationships. We build on previous works describing ontology-based trajectory representations and tackle the knowledge discovery task using AMIE, a well-known state-of-the-art KB rule mining algorithm. This approach mines patterns in the form of Horn rules, which allows us to investigate associations between time, spatial, and semantic relations interlinking trajectory events. We show that representations previously proposed in the Semantic Trajectory community are not suitable to be directly mined by this approach. However, they can be easily extended to power the AMIE algorithm. We also describe and address different issues that arise when using a domain-agnostic mining algorithm. The proposed data pipeline mines interesting patterns in experiments using Foursquare datasets. Nonetheless, there is a large number of rules which state facts that are too general. We build on these issues and argue in favor of the design of a domain-specific mining algorithm. We discuss future opportunities based on the acquired experience and experiments. Our approach shows how the Semantic Trajectory and Knowledge Base Refinement communities have built in recent years a large number of representations and mining approaches that could be put together to mine rules with rich semantic expressiveness from semantic data.Diferentes tecnologias e aspectos socioculturais em nosso dia a dia permitem a aquisição de dados de mobilidade de pessoas. O mesmo se aplica a outros objetos móveis, como pássaros utilizando rastreadores GPS e furacões analisados via satélite. Embora estes dados de localização espacial e temporal sejam úteis em muitas aplicações, a comunidade de dados de trajetórias reconheceu há tempos a necessidade de aspectos semânticos para capturar a complexidade dos comportamentos de humanos e de outros objetos. Nesse contexto, foram propostas as Trajetórias Semânticas, que se baseiam em trajetórias espaço-temporais enriquecidas com anotações semânticas e possivelmente interligadas com dados externos. Por conta disso, muitos trabalhos incorporam conceitos e tecnologias da Web Semântica para lidar com a complexidade de representar, mesclar e consultar dados heterogêneos. Esses trabalhos geralmente utilizam ontologias para manipular conceitos como Objetos Móveis, Trajetórias, Paradas (Stops) e Movimentos (Moves), bem como os diferentes aspectos semânticos relacionados a cada um deles. Entretanto, não é possível encontrar na literatura trabalhos que explorem a mineração de padrões aplicada diretamente nestas representações baseadas em ontologia. Em vez disso, os esforços atuais utilizam algoritmos de mineração de regras de associação, como o Apriori, que requerem dados proposicionais. Esta dissertação explora algoritmos do campo do Refinamento de Bases de Conhecimento de modo a extrair padrões que tirem proveito das relações armazenadas em uma ontologia de Trajetórias Semânticas. A proposta original desses algoritmos é a inferência de novas entidades e relacionamentos em Bases de Conhecimento (KBs, do inglês Knowledge Bases), como a YAGO e a DBPedia, utilizando-se para isso os fatos já armazenados nas bases. Neste trabalho, utiliza-se a ferramenta AMIE, um representante do estado da arte na mineração de regras em KBs, que permite a extração eficiente de padrões na forma de Regras de Horn. No contexto de Trajetórias Semânticas, isso representa a mineração de associações entre as relações temporais, espaciais e semânticas que interligam eventos em uma base de trajetórias. O pipeline de dados proposto é capaz de extrair padrões interessantes em experimentos utilizando conjuntos de dados do Foursquare. No entanto, a utilização de um algoritmo agnóstico de domínio acaba por minerar um grande número de regras que definem fatos que são muito gerais. Construímos técnicas para avançar sobre essas questões e argumentamos a favor do desenvolvimento de um algoritmo de mineração específico de domínio. Além disso, a abordagem investigada mostra como as comunidades de Trajetórias Semânticas e Refinamento de KBs construíram um grande número de representações e abordagens de mineração que poderiam ser reunidas para extrair padrões com rica expressividade semântica a partir de dados semânticos.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)CAPES: 001CNPq: 130790/2020-6FAPESP: #2017/24340-6engUniversidade Federal de São CarlosCâmpus São CarlosPrograma de Pós-Graduação em Ciência da Computação - PPGCCUFSCarAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessSemantic trajectorySemantic data miningAssociation rule miningKnowledge baseOntologyTrajetória semânticaMineração de regras de associaçãoBase de conhecimentoOntologiaCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAOTowards semantic association rules mining from ontology-based semantic trajectoriesEm direção a mineração de regras de associações semânticas aplicada a trajetórias semânticas baseadas em ontologiasinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis6006009185a24d-3ee1-48a1-82f2-dad58a6b653ereponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARORIGINALstsarm final.pdfstsarm final.pdfDissertaçãoapplication/pdf2172375https://repositorio.ufscar.br/bitstream/ufscar/14554/3/stsarm%20final.pdf3dd1d2fe3e2535542280ac3fe3879cb3MD53[assinado]PPGCC_Template_dec_BCO.pdf[assinado]PPGCC_Template_dec_BCO.pdfCarta Comprovanteapplication/pdf99770https://repositorio.ufscar.br/bitstream/ufscar/14554/2/%5bassinado%5dPPGCC_Template_dec_BCO.pdf5d7bd84df118af022739fecbe030fe31MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufscar.br/bitstream/ufscar/14554/4/license_rdfe39d27027a6cc9cb039ad269a5db8e34MD54TEXTstsarm final.pdf.txtstsarm final.pdf.txtExtracted texttext/plain288566https://repositorio.ufscar.br/bitstream/ufscar/14554/5/stsarm%20final.pdf.txt3a51314c178c6699b17cebbf16de46c9MD55[assinado]PPGCC_Template_dec_BCO.pdf.txt[assinado]PPGCC_Template_dec_BCO.pdf.txtExtracted texttext/plain1464https://repositorio.ufscar.br/bitstream/ufscar/14554/7/%5bassinado%5dPPGCC_Template_dec_BCO.pdf.txt450553bd1afe978e700cb14e79b57bbaMD57THUMBNAILstsarm final.pdf.jpgstsarm final.pdf.jpgIM Thumbnailimage/jpeg8522https://repositorio.ufscar.br/bitstream/ufscar/14554/6/stsarm%20final.pdf.jpg2629d96b8c662abe9577b4f7288910dbMD56[assinado]PPGCC_Template_dec_BCO.pdf.jpg[assinado]PPGCC_Template_dec_BCO.pdf.jpgIM Thumbnailimage/jpeg13352https://repositorio.ufscar.br/bitstream/ufscar/14554/8/%5bassinado%5dPPGCC_Template_dec_BCO.pdf.jpg2c64b0a63ca25b121b70e1486fa74f3fMD58ufscar/145542023-09-18 18:32:12.595oai:repositorio.ufscar.br:ufscar/14554Repositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestopendoar:43222023-09-18T18:32:12Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.eng.fl_str_mv |
Towards semantic association rules mining from ontology-based semantic trajectories |
dc.title.alternative.por.fl_str_mv |
Em direção a mineração de regras de associações semânticas aplicada a trajetórias semânticas baseadas em ontologias |
title |
Towards semantic association rules mining from ontology-based semantic trajectories |
spellingShingle |
Towards semantic association rules mining from ontology-based semantic trajectories Petri, Antonio Carlos Falcão Semantic trajectory Semantic data mining Association rule mining Knowledge base Ontology Trajetória semântica Mineração de regras de associação Base de conhecimento Ontologia CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO |
title_short |
Towards semantic association rules mining from ontology-based semantic trajectories |
title_full |
Towards semantic association rules mining from ontology-based semantic trajectories |
title_fullStr |
Towards semantic association rules mining from ontology-based semantic trajectories |
title_full_unstemmed |
Towards semantic association rules mining from ontology-based semantic trajectories |
title_sort |
Towards semantic association rules mining from ontology-based semantic trajectories |
author |
Petri, Antonio Carlos Falcão |
author_facet |
Petri, Antonio Carlos Falcão |
author_role |
author |
dc.contributor.authorlattes.por.fl_str_mv |
http://lattes.cnpq.br/3663434860348677 |
dc.contributor.author.fl_str_mv |
Petri, Antonio Carlos Falcão |
dc.contributor.advisor1.fl_str_mv |
Silva, Diego Furtado |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/7662777934692986 |
dc.contributor.authorID.fl_str_mv |
a61505e8-e235-4ccd-b98c-08fe0cee428d |
contributor_str_mv |
Silva, Diego Furtado |
dc.subject.eng.fl_str_mv |
Semantic trajectory Semantic data mining Association rule mining Knowledge base Ontology |
topic |
Semantic trajectory Semantic data mining Association rule mining Knowledge base Ontology Trajetória semântica Mineração de regras de associação Base de conhecimento Ontologia CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO |
dc.subject.por.fl_str_mv |
Trajetória semântica Mineração de regras de associação Base de conhecimento Ontologia |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO |
description |
Different technologies and social-cultural aspects of our lives have allowed the acquisition of people's mobility data. The same applies to other moving objects, such as birds with GPS trackers and hurricanes with real-time satellite data. Although these raw positioning and timings are useful in many applications, it has been long recognized by the Trajectory Data community that semantics are required to capture the complexity of humans' and other objects' behaviors. Semantic Trajectories were proposed in this context as raw trajectories enriched with semantic annotations and possibly interlinked with external data. Based on these requirements, many works incorporate concepts and technologies from the Semantic Web to deal with the complexity of merging, representing, and querying heterogeneous data. They usually use ontologies to represent and manipulate concepts such as Moving Objects, Trajectories, Stops and Moves, and semantic aspects related to each of them. Nonetheless, we find that no previous work has explored mining patterns from these ontology-based representations. On the contrary, current efforts use standard association rule mining algorithms, such as Apriori, which require propositional data represented as Boolean feature vectors. To mine patterns aware of the semantic relations in a Semantic Trajectory ontology, we explore algorithms from the Knowledge Base Refinement field. These methods were proposed to use real-world facts represented in Knowledge Bases such as YAGO and DBPedia to infer new entities and relationships. We build on previous works describing ontology-based trajectory representations and tackle the knowledge discovery task using AMIE, a well-known state-of-the-art KB rule mining algorithm. This approach mines patterns in the form of Horn rules, which allows us to investigate associations between time, spatial, and semantic relations interlinking trajectory events. We show that representations previously proposed in the Semantic Trajectory community are not suitable to be directly mined by this approach. However, they can be easily extended to power the AMIE algorithm. We also describe and address different issues that arise when using a domain-agnostic mining algorithm. The proposed data pipeline mines interesting patterns in experiments using Foursquare datasets. Nonetheless, there is a large number of rules which state facts that are too general. We build on these issues and argue in favor of the design of a domain-specific mining algorithm. We discuss future opportunities based on the acquired experience and experiments. Our approach shows how the Semantic Trajectory and Knowledge Base Refinement communities have built in recent years a large number of representations and mining approaches that could be put together to mine rules with rich semantic expressiveness from semantic data. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-07-08T11:46:10Z |
dc.date.available.fl_str_mv |
2021-07-08T11:46:10Z |
dc.date.issued.fl_str_mv |
2021-02-09 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
PETRI, Antonio Carlos Falcão. Towards semantic association rules mining from ontology-based semantic trajectories. 2021. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2021. Disponível em: https://repositorio.ufscar.br/handle/ufscar/14554. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufscar.br/handle/ufscar/14554 |
identifier_str_mv |
PETRI, Antonio Carlos Falcão. Towards semantic association rules mining from ontology-based semantic trajectories. 2021. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2021. Disponível em: https://repositorio.ufscar.br/handle/ufscar/14554. |
url |
https://repositorio.ufscar.br/handle/ufscar/14554 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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600 600 |
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9185a24d-3ee1-48a1-82f2-dad58a6b653e |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de São Carlos Câmpus São Carlos |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Ciência da Computação - PPGCC |
dc.publisher.initials.fl_str_mv |
UFSCar |
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Universidade Federal de São Carlos Câmpus São Carlos |
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