Pub Date : 2022-08-31DOI: 10.5220/0010689700003064
A. Lambert, M. D. Hina, Celine Barth, Assia Soukane, A. Ramdane-Cherif
Road accidents have become the eight leading cause of death all over the world. Lots of these accidents are due to a driver's inattention or lack of focus, due to fatigue. Various factors cause driver's fatigue. This paper considers all the measureable data that manifest driver's fatigue, namely those manifested in the vehicle measureable data while driving as well as the driver's physical and physiological data. Each of the three main factors are further subdivided into smaller details. For example, the vehicle's data is composed of the values obtained from the steering wheel's angle, yaw angle, the position on the lane, and the speed and acceleration of the vehicle while moving. Ontological knowledge and rules for driver fatigue detection are to be integrated into an intelligent system so that on the first sign of dangerous level of fatigue is detected, a warning notification is sent to the driver. This work is intended to contribute to safe road driving.
{"title":"Modelling and Detection of Driver's Fatigue using Ontology","authors":"A. Lambert, M. D. Hina, Celine Barth, Assia Soukane, A. Ramdane-Cherif","doi":"10.5220/0010689700003064","DOIUrl":"https://doi.org/10.5220/0010689700003064","url":null,"abstract":"Road accidents have become the eight leading cause of death all over the world. Lots of these accidents are due to a driver's inattention or lack of focus, due to fatigue. Various factors cause driver's fatigue. This paper considers all the measureable data that manifest driver's fatigue, namely those manifested in the vehicle measureable data while driving as well as the driver's physical and physiological data. Each of the three main factors are further subdivided into smaller details. For example, the vehicle's data is composed of the values obtained from the steering wheel's angle, yaw angle, the position on the lane, and the speed and acceleration of the vehicle while moving. Ontological knowledge and rules for driver fatigue detection are to be integrated into an intelligent system so that on the first sign of dangerous level of fatigue is detected, a warning notification is sent to the driver. This work is intended to contribute to safe road driving.","PeriodicalId":356318,"journal":{"name":"International Conference on Knowledge Engineering and Ontology Development","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134506612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.5220/0010122400860097
T. Phan, Brian Lam, M. Hashmi, Yongsun Choi
Automating information extraction from legal documents and formalising them into a machine understandable format has long been an integral challenge to legal reasoning. Most approaches in the past consist of highly complex solutions that use annotated syntactic structures and grammar to distil rules. The current research trend is to utilise state-of-the-art natural language processing (NLP) approaches to automate these tasks, with minimum human interference. In this paper, based on its functional aspects, we propose a legal taxonomy of semantic types in Korean legislation, such as definitional provision, deeming provision, penalty, obligation, permission, prohibition, etc. In addition to this, a NLP classifier has been developed to facilitate the automated legal norms classification process and an overall F1 score of 0.97 has been achieved.
{"title":"Towards Construction of Legal Ontology for Korean Legislation","authors":"T. Phan, Brian Lam, M. Hashmi, Yongsun Choi","doi":"10.5220/0010122400860097","DOIUrl":"https://doi.org/10.5220/0010122400860097","url":null,"abstract":"Automating information extraction from legal documents and formalising\u0000them into a machine understandable format has long been an integral challenge\u0000to legal reasoning. Most approaches in the past consist of highly complex\u0000solutions that use annotated syntactic structures and grammar to distil rules.\u0000The current research trend is to utilise state-of-the-art natural language processing (NLP)\u0000approaches to automate these tasks, with minimum human interference. In this\u0000paper, based on its functional aspects, we propose a legal taxonomy of semantic\u0000types in Korean legislation, such as definitional provision, deeming provision,\u0000penalty, obligation, permission, prohibition, etc. In addition to this, a NLP classifier has\u0000been developed to facilitate the automated legal norms classification process\u0000and an overall F1 score of 0.97 has been achieved.","PeriodicalId":356318,"journal":{"name":"International Conference on Knowledge Engineering and Ontology Development","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132281431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-02DOI: 10.5220/0010147202750282
M. E. Ghosh, C. Zanni-Merk, Nicolas Delestre, Jean-Philippe Kotowicz, H. Abdulrab
Topic ontologies are recently gaining much importance in several domains. Their purpose is to identify the themes necessary to describe the knowledge structure of an application domain. Meanwhile, their development from scratch is hard and time consuming task. This paper discusses the development a topic-specific ontology, named Topic-OPA, for modeling topics of old press articles. Topic-OPA is extracted from the open knowledge graph Wikidata by the application of a SPARQL-based fully automatic approach. The development process of Topic-OPA depends mainly on a set of disambiguated named entities representing the articles. Each named entity is unambiguously identified by a Wikidata URI. In contrast to existent topic ontologies, which are limited to taxonomies, the structure of Topic-OPA is composed of hierarchical and non-hierarchical schemes. The domain application of this work is the old french newspaper Le Matin. Finally, an evaluation process is performed to assess the structure quality of Topic-OPA.
{"title":"Topic-OPA: A Topic Ontology for Modeling Topics of Old Press Articles","authors":"M. E. Ghosh, C. Zanni-Merk, Nicolas Delestre, Jean-Philippe Kotowicz, H. Abdulrab","doi":"10.5220/0010147202750282","DOIUrl":"https://doi.org/10.5220/0010147202750282","url":null,"abstract":"Topic ontologies are recently gaining much importance in several domains. Their purpose is to identify the themes necessary to describe the knowledge structure of an application domain. Meanwhile, their development from scratch is hard and time consuming task. This paper discusses the development a topic-specific ontology, named Topic-OPA, for modeling topics of old press articles. Topic-OPA is extracted from the open knowledge graph Wikidata by the application of a SPARQL-based fully automatic approach. The development process of Topic-OPA depends mainly on a set of disambiguated named entities representing the articles. Each named entity is unambiguously identified by a Wikidata URI. In contrast to existent topic ontologies, which are limited to taxonomies, the structure of Topic-OPA is composed of hierarchical and non-hierarchical schemes. The domain application of this work is the old french newspaper Le Matin. Finally, an evaluation process is performed to assess the structure quality of Topic-OPA.","PeriodicalId":356318,"journal":{"name":"International Conference on Knowledge Engineering and Ontology Development","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116838818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.5220/0010147902910298
J. Olszewska
: Software testing is an expanding area which presents an increasing complexity. Indeed, on one hand, there is the development of technologies such as Software Testing as a Service (TaaS), and on the other hand, there is a growing number of Artificial Intelligence (AI)-based softwares. Hence, this work is about the development of an ontological framework for AI-softwares’ Testing (AI-T), which domain covers both software testing and explainable artificial intelligence; the goal being to produce an ontology which guides the testing of AI softwares, in an effective and interoperable way. For this purpose, AI-T ontology includes temporal interval logic modelling of the software testing process as well as ethical principle formalization and has been built using the Enterprise Ontology (EO) methodology. Our resulting AI-T ontology proposes both conceptual and implementation models and contains 708 terms and 706 axioms.
{"title":"AI-T: Software Testing Ontology for AI-based Systems","authors":"J. Olszewska","doi":"10.5220/0010147902910298","DOIUrl":"https://doi.org/10.5220/0010147902910298","url":null,"abstract":": Software testing is an expanding area which presents an increasing complexity. Indeed, on one hand, there is the development of technologies such as Software Testing as a Service (TaaS), and on the other hand, there is a growing number of Artificial Intelligence (AI)-based softwares. Hence, this work is about the development of an ontological framework for AI-softwares’ Testing (AI-T), which domain covers both software testing and explainable artificial intelligence; the goal being to produce an ontology which guides the testing of AI softwares, in an effective and interoperable way. For this purpose, AI-T ontology includes temporal interval logic modelling of the software testing process as well as ethical principle formalization and has been built using the Enterprise Ontology (EO) methodology. Our resulting AI-T ontology proposes both conceptual and implementation models and contains 708 terms and 706 axioms.","PeriodicalId":356318,"journal":{"name":"International Conference on Knowledge Engineering and Ontology Development","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115008186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-02DOI: 10.5220/0010112500760085
Biswanath Dutta, Michael DeBellis
The COviD-19 Ontology for cases and patient information (CODO) provides a model for the collection and analysis of data about the COVID-19 pandemic. The ontology provides a standards-based open-source model that facilitates the integration of data from heterogeneous data sources. The ontology was designed by analysing disparate COVID-19 data sources such as datasets, literature, services, etc. The ontology follows the best practices for vocabularies by re-using concepts from other leading vocabularies and by using the W3C standards RDF, OWL, SWRL, and SPARQL. The ontology already has one independent user and has incorporated real-world data from the government of India.
{"title":"CODO: An Ontology for Collection and Analysis of Covid-19 Data","authors":"Biswanath Dutta, Michael DeBellis","doi":"10.5220/0010112500760085","DOIUrl":"https://doi.org/10.5220/0010112500760085","url":null,"abstract":"The COviD-19 Ontology for cases and patient information (CODO) provides a model for the collection and analysis of data about the COVID-19 pandemic. The ontology provides a standards-based open-source model that facilitates the integration of data from heterogeneous data sources. The ontology was designed by analysing disparate COVID-19 data sources such as datasets, literature, services, etc. The ontology follows the best practices for vocabularies by re-using concepts from other leading vocabularies and by using the W3C standards RDF, OWL, SWRL, and SPARQL. The ontology already has one independent user and has incorporated real-world data from the government of India.","PeriodicalId":356318,"journal":{"name":"International Conference on Knowledge Engineering and Ontology Development","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131275865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-17DOI: 10.5220/0008167101640173
Mohammed Alliheedi, Yetian Wang, Robert E. Mercer
Ontologies must provide the entities, concepts, and relations required by the domain being represented. The domain of interest in this paper is the biochemistry experimental procedure. The ontology language being used is OWL-DL. OWL-DL was adopted due to its well-balanced flexibility among expressiveness (e.g., class description, cardinality restriction, etc.), completeness, and decidability. These procedures are composed of procedure steps which can be represented as sequences. Sequences are composed of totally ordered, partially ordered, and alternative subsequences. Subsequences can be represented with two relations, directlyFollows and directlyPrecedes that are used to represent sequences. Alternative subsequences can be generated by composing a oneOf function in OWL-DL, referred to it as optionalStepOf in this work, which is a simple generalization of exclusiveOR. Alkaline Agarose Gel Electrophoresis, a biochemistry procedure, is described and examples of these subsequences are provided.
{"title":"Biochemistry Procedure-oriented Ontology: A Case Study","authors":"Mohammed Alliheedi, Yetian Wang, Robert E. Mercer","doi":"10.5220/0008167101640173","DOIUrl":"https://doi.org/10.5220/0008167101640173","url":null,"abstract":"Ontologies must provide the entities, concepts, and relations required by the domain being represented. The domain of interest in this paper is the biochemistry experimental procedure. The ontology language being used is OWL-DL. OWL-DL was adopted due to its well-balanced flexibility among expressiveness (e.g., class description, cardinality restriction, etc.), completeness, and decidability. These procedures are composed of procedure steps which can be represented as sequences. Sequences are composed of totally ordered, partially ordered, and alternative subsequences. Subsequences can be represented with two relations, directlyFollows and directlyPrecedes that are used to represent sequences. Alternative subsequences can be generated by composing a oneOf function in OWL-DL, referred to it as optionalStepOf in this work, which is a simple generalization of exclusiveOR. Alkaline Agarose Gel Electrophoresis, a biochemistry procedure, is described and examples of these subsequences are provided.","PeriodicalId":356318,"journal":{"name":"International Conference on Knowledge Engineering and Ontology Development","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116771678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-17DOI: 10.5220/0008347803430350
Aikaterini Nikolaidou, M. Lazaridis, T. Semertzidis, A. Axenopoulos, P. Daras
It is a challenge to aggregate and analyze data from heterogeneous social media sources not only for businesses and organizations but also for Law Enforcement Agencies. The latter’s core objectives are to monitor criminal and terrorist related activities and to identify the ”key players” in various networks. In this paper, a framework for homogenizing and exploiting data from multiple sources is presented. Moreover, as part of the framework, an ontology that reflects today’s social media perceptions is introduced. Data from multiple sources is transformed into a labeled property graph and stored in a graph database in a homogenized way based on the proposed ontology. The result is a cross-source analysis system where end-users can explore different scenarios and draw conclusions through a library of predefined query placeholders that focus on forensic investigation. The framework is evaluated on the Stormfront dataset, a radical right, web community. Finally, the benefits of applying the proposed framework to discover and visualize the relationships between the Stormfront profiles are presented.
{"title":"Forensic Analysis of Heterogeneous Social Media Data","authors":"Aikaterini Nikolaidou, M. Lazaridis, T. Semertzidis, A. Axenopoulos, P. Daras","doi":"10.5220/0008347803430350","DOIUrl":"https://doi.org/10.5220/0008347803430350","url":null,"abstract":"It is a challenge to aggregate and analyze data from heterogeneous social media sources not only for businesses and organizations but also for Law Enforcement Agencies. The latter’s core objectives are to monitor criminal and terrorist related activities and to identify the ”key players” in various networks. In this paper, a framework for homogenizing and exploiting data from multiple sources is presented. Moreover, as part of the framework, an ontology that reflects today’s social media perceptions is introduced. Data from multiple sources is transformed into a labeled property graph and stored in a graph database in a homogenized way based on the proposed ontology. The result is a cross-source analysis system where end-users can explore different scenarios and draw conclusions through a library of predefined query placeholders that focus on forensic investigation. The framework is evaluated on the Stormfront dataset, a radical right, web community. Finally, the benefits of applying the proposed framework to discover and visualize the relationships between the Stormfront profiles are presented.","PeriodicalId":356318,"journal":{"name":"International Conference on Knowledge Engineering and Ontology Development","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126088158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-17DOI: 10.5220/0008169301740184
Andrew LeClair, Ridha Khédri, Alicia Marinache
This paper formalizes the graphical modularization technique, View Traversal, for an ontology-based system represented using the Domain Information System (DIS). Our work is motivated by the need for autonomous agents, within an ontology-based system, to automatically create their own views of the ontology to address the problems of ontology evolution and data integration found in an enterprise setting. Through DIS, we explore specific ontologies that give Cartesian perspectives of the domain, which allows modularization to be a means for agents to extract views of specific combinations of data. The theory of ideals from Boolean algebra is used to formalize a module. Then, with the use of homomorphisms, the quantity of knowledge within the module can be measured. More specifically, through the first isomorphism theorem, we establish that the loss of information is quantified by the kernel of the homomorphism. This constitutes a foundational step towards theories related to reasoning on partial domain knowledge, and is important for applications where an agent needs to quickly extract a view that contains a specific set of knowledge.
{"title":"Toward Measuring Knowledge Loss due to Ontology Modularization","authors":"Andrew LeClair, Ridha Khédri, Alicia Marinache","doi":"10.5220/0008169301740184","DOIUrl":"https://doi.org/10.5220/0008169301740184","url":null,"abstract":"This paper formalizes the graphical modularization technique, View Traversal, for an ontology-based system represented using the Domain Information System (DIS). Our work is motivated by the need for autonomous agents, within an ontology-based system, to automatically create their own views of the ontology to address the problems of ontology evolution and data integration found in an enterprise setting. Through DIS, we explore specific ontologies that give Cartesian perspectives of the domain, which allows modularization to be a means for agents to extract views of specific combinations of data. The theory of ideals from Boolean algebra is used to formalize a module. Then, with the use of homomorphisms, the quantity of knowledge within the module can be measured. More specifically, through the first isomorphism theorem, we establish that the loss of information is quantified by the kernel of the homomorphism. This constitutes a foundational step towards theories related to reasoning on partial domain knowledge, and is important for applications where an agent needs to quickly extract a view that contains a specific set of knowledge.","PeriodicalId":356318,"journal":{"name":"International Conference on Knowledge Engineering and Ontology Development","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122131366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-17DOI: 10.5220/0008169601850194
Imen Sarray, A. Salah
Much research has been undertaken to facilitate the construction of SPARQL queries, while other research has attempted to facilitate the construction of the RDF dataset schema to understand the structure of RDF datasets. However, there is no effective approach that brings together these two complementary objectives. This work is an effort in this direction. We propose an approach that allows assisted SPARQL query composition. Linked data interrogation is not only difficult because it requires mastering a query language such as SPARQL, but mainly because RDF datasets do not have an explicit schema as what you can expect in relational databases. This paper provides two complimentary solutions: synthesis of an interrogation-oriented schema and a form-based RDF Query construction tool, name EXPLO-RDF.
{"title":"Assisted Composition of Linked Data Queries","authors":"Imen Sarray, A. Salah","doi":"10.5220/0008169601850194","DOIUrl":"https://doi.org/10.5220/0008169601850194","url":null,"abstract":"Much research has been undertaken to facilitate the construction of SPARQL queries, while other research has attempted to facilitate the construction of the RDF dataset schema to understand the structure of RDF datasets. However, there is no effective approach that brings together these two complementary objectives. This work is an effort in this direction. We propose an approach that allows assisted SPARQL query composition. Linked data interrogation is not only difficult because it requires mastering a query language such as SPARQL, but mainly because RDF datasets do not have an explicit schema as what you can expect in relational databases. This paper provides two complimentary solutions: synthesis of an interrogation-oriented schema and a form-based RDF Query construction tool, name EXPLO-RDF.","PeriodicalId":356318,"journal":{"name":"International Conference on Knowledge Engineering and Ontology Development","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133658756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-17DOI: 10.5220/0008387804950499
Citlalli Mejía-Almonte, J. Collado-Vides
Here we present a formal ontological model that explicitly represents regulatory interactions among the main objects involved in transcriptional regulation in bacteria. These formal relations allow the inference of gene regulation physiology from gene regulation mechanisms. The automatically instantiated classes can be used to assist in the mechanistic interpretation of gene expression experiments done at the physiological level, such as RNA-seq. This is the first step to develop a more comprehensive ontology focused on prokaryotic gene regulation. The ontology is available at https://github.com/prokaryotic-regulation-ontology
{"title":"Towards the Prokaryotic Regulation Ontology: An Ontological Model to Infer Gene Regulation Physiology from Mechanisms in Bacteria","authors":"Citlalli Mejía-Almonte, J. Collado-Vides","doi":"10.5220/0008387804950499","DOIUrl":"https://doi.org/10.5220/0008387804950499","url":null,"abstract":"Here we present a formal ontological model that explicitly represents regulatory interactions among the main objects involved in transcriptional regulation in bacteria. These formal relations allow the inference of gene regulation physiology from gene regulation mechanisms. The automatically instantiated classes can be used to assist in the mechanistic interpretation of gene expression experiments done at the physiological level, such as RNA-seq. This is the first step to develop a more comprehensive ontology focused on prokaryotic gene regulation. The ontology is available at https://github.com/prokaryotic-regulation-ontology","PeriodicalId":356318,"journal":{"name":"International Conference on Knowledge Engineering and Ontology Development","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124503143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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