Pub Date : 2015-03-02DOI: 10.1109/ICOSC.2015.7050848
M. Alruily, Mohammad A. Alghamdi
Most Arabic systems developed for newspaper information extraction relate to events that occurred in the past. This paper presents a proposal for developing a web-based system that will be able to regularly collect news reports from Arabic newspaper websites, and then be able to extract information relating to future events, e.g. event type, date and location. Also, the system will be able to deposit the extracted data in an online database in order to enable users to access them. The proposed approach is based on Arabic grammar and on dependency relationships.
{"title":"Extracting information of future events from Arabic newspapers: an overview","authors":"M. Alruily, Mohammad A. Alghamdi","doi":"10.1109/ICOSC.2015.7050848","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050848","url":null,"abstract":"Most Arabic systems developed for newspaper information extraction relate to events that occurred in the past. This paper presents a proposal for developing a web-based system that will be able to regularly collect news reports from Arabic newspaper websites, and then be able to extract information relating to future events, e.g. event type, date and location. Also, the system will be able to deposit the extracted data in an online database in order to enable users to access them. The proposed approach is based on Arabic grammar and on dependency relationships.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126538245","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050821
H. Yamaguchi, M. Gotaishi, S. Tsujii
The ultimate objective of the problem solving system is to provide an interrelated framework for prospective users to facilitate their work, such as biological and biomedical knowledge retrieval, discovery, capture. In addition to this objective, there is demand for secure and practical computing algorithms which address the challenge to safely outsource data processing onto remote computing resources. This allows users to confidently outsource computation over sensitive information from the security level of the remote delegate. In this paper, we present the computing algorithms which preserve privacy of users and confidentiality of service providers in the cloud environment.
{"title":"Secure problems solving scheme","authors":"H. Yamaguchi, M. Gotaishi, S. Tsujii","doi":"10.1109/ICOSC.2015.7050821","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050821","url":null,"abstract":"The ultimate objective of the problem solving system is to provide an interrelated framework for prospective users to facilitate their work, such as biological and biomedical knowledge retrieval, discovery, capture. In addition to this objective, there is demand for secure and practical computing algorithms which address the challenge to safely outsource data processing onto remote computing resources. This allows users to confidently outsource computation over sensitive information from the security level of the remote delegate. In this paper, we present the computing algorithms which preserve privacy of users and confidentiality of service providers in the cloud environment.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125664452","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050798
L. Sharma, Namita Mittal
Semantic parsing is still a challenging problem for open domain question answering. In semantic parsing, questions are mapped with their meaning representations. These representations are matched with feasible answers in knowledge bases. In Knowledge bases (e.g. Freebase), knowledge is stored in the form of Topics. For a successful answer extraction from Freebase, it is required to correctly identify the Topic node (or Topic word) of the question and retrieve every type and property associated with this Topic node. In this paper, a Topic Node Identification (TNI) algorithm is proposed for correctly identifying question Topic and Domain Word Identification (DWI) algorithm is proposed for correctly identifying domain of the Topic node. After domain identification the Topic node is further expanded for its all types and properties. Out of all types identified, one of the type and associated property is likely to be an answer of the question. TWI and DWI algorithms use techniques i.e. proposed rulebased and machine learning approach with the help of question dependency parser. Results of proposed approach outperform state of art approaches.
{"title":"Topic oriented semantic parsing","authors":"L. Sharma, Namita Mittal","doi":"10.1109/ICOSC.2015.7050798","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050798","url":null,"abstract":"Semantic parsing is still a challenging problem for open domain question answering. In semantic parsing, questions are mapped with their meaning representations. These representations are matched with feasible answers in knowledge bases. In Knowledge bases (e.g. Freebase), knowledge is stored in the form of Topics. For a successful answer extraction from Freebase, it is required to correctly identify the Topic node (or Topic word) of the question and retrieve every type and property associated with this Topic node. In this paper, a Topic Node Identification (TNI) algorithm is proposed for correctly identifying question Topic and Domain Word Identification (DWI) algorithm is proposed for correctly identifying domain of the Topic node. After domain identification the Topic node is further expanded for its all types and properties. Out of all types identified, one of the type and associated property is likely to be an answer of the question. TWI and DWI algorithms use techniques i.e. proposed rulebased and machine learning approach with the help of question dependency parser. Results of proposed approach outperform state of art approaches.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117272873","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050773
F. Rodrigues, C. Flores, L. Rotta
Ontologies are an important tool in knowledge representation. Currently, one of the most popular languages to model ontologies is the Web Ontology Language (OWL). Such language is in its second version (OWL 2), which was designed to overcome some issues in expressivity, syntax, semantics and other practical aspects of the previous version. However, it still lacks some constructs that would be useful in some domains and situations. One of such constructs is what could be called “universal relationship” (i.e. a relationship with maximal degree of universality, so that, if established between two classes, states that every individual of a class is related to every individual of the other by this particular relationship). Therefore, this work brings an ontology design pattern to address such modeling issue. It requires OWL 2 features to be implemented, but dispenses any other external resources to handle the problem. The article presents the structure of the pattern and its implementation steps, as well as an example from the biomedical field.
{"title":"An ontology design pattern to represent universal relationships","authors":"F. Rodrigues, C. Flores, L. Rotta","doi":"10.1109/ICOSC.2015.7050773","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050773","url":null,"abstract":"Ontologies are an important tool in knowledge representation. Currently, one of the most popular languages to model ontologies is the Web Ontology Language (OWL). Such language is in its second version (OWL 2), which was designed to overcome some issues in expressivity, syntax, semantics and other practical aspects of the previous version. However, it still lacks some constructs that would be useful in some domains and situations. One of such constructs is what could be called “universal relationship” (i.e. a relationship with maximal degree of universality, so that, if established between two classes, states that every individual of a class is related to every individual of the other by this particular relationship). Therefore, this work brings an ontology design pattern to address such modeling issue. It requires OWL 2 features to be implemented, but dispenses any other external resources to handle the problem. The article presents the structure of the pattern and its implementation steps, as well as an example from the biomedical field.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123030772","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050782
S. Manna, Xing Hu, Robert Correa
Due to the rapid advancement of the internet technology, there is proliferation of textual data, as a result of which automatic summarization has become one of the useful means of coping with the problem of information overload. These textual data are not just in English; thus researchers started focusing on multilingual summarization platforms, so that single framework can be used to cope with different languages. Chinese being another widely spoken language, in this paper, we present an extension of Subjective Logic summarization framework (SubSum) [1], for Chinese. SubSum extracts significant sentences from documents to form extractive summaries. Quantifying uncertainty is the key advantage of SubSum over commonly used summarization methods. The main aim of this work is to show how well SubSum can be adapted to a completely different language, without making changes to the core framework. Moreover, extensive experiments on benchmark datasets demonstrate the effectiveness of SubSum applied for Chinese summarization.
{"title":"Towards multi-lingual adaptability of subjective logic based document summarization: A case study with Chinese documents","authors":"S. Manna, Xing Hu, Robert Correa","doi":"10.1109/ICOSC.2015.7050782","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050782","url":null,"abstract":"Due to the rapid advancement of the internet technology, there is proliferation of textual data, as a result of which automatic summarization has become one of the useful means of coping with the problem of information overload. These textual data are not just in English; thus researchers started focusing on multilingual summarization platforms, so that single framework can be used to cope with different languages. Chinese being another widely spoken language, in this paper, we present an extension of Subjective Logic summarization framework (SubSum) [1], for Chinese. SubSum extracts significant sentences from documents to form extractive summaries. Quantifying uncertainty is the key advantage of SubSum over commonly used summarization methods. The main aim of this work is to show how well SubSum can be adapted to a completely different language, without making changes to the core framework. Moreover, extensive experiments on benchmark datasets demonstrate the effectiveness of SubSum applied for Chinese summarization.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"898 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132317436","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050809
Lydia Odilinye, F. Popowich, Evan Zhang, J. Nesbit, P. Winne
Automatic question generation from text has been used and adapted to online and self-directed learning platforms. We incorporate methods into the automatic question generation process that are designed to improve question quality by aligning them to the specified pedagogical goals and to a learner's model. This is achieved by extracting, ranking and filtering relevant sentences in the given learning document as well as the questions automatically generated by their semantic associations to the learner model and instructor goals. We propose evaluation techniques for assessing the quality of the questions generated using both human and automatic evaluation.
{"title":"Aligning automatically generated questions to instructor goals and learner behaviour","authors":"Lydia Odilinye, F. Popowich, Evan Zhang, J. Nesbit, P. Winne","doi":"10.1109/ICOSC.2015.7050809","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050809","url":null,"abstract":"Automatic question generation from text has been used and adapted to online and self-directed learning platforms. We incorporate methods into the automatic question generation process that are designed to improve question quality by aligning them to the specified pedagogical goals and to a learner's model. This is achieved by extracting, ranking and filtering relevant sentences in the given learning document as well as the questions automatically generated by their semantic associations to the learner model and instructor goals. We propose evaluation techniques for assessing the quality of the questions generated using both human and automatic evaluation.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132352711","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050853
L. Tuovinen, Jari Kahelin, J. Röning
Semantic metadata has many uses in online virtual environments, but the technology for acquiring and managing metadata is not yet fully developed. This paper presents a partial solution that combines and extends traditional top-down and bottom-up approaches to semantic annotation. The paper proposes a metadata architecture composed of three layers, each of which has a metadata store and an interface for importing metadata from a specific group of providers. Under certain conditions, metadata can be propagated between layers, enabling it to be shared with other users of the same application or other applications based on the same virtual reality model. A proof-of-concept implementation demonstrates the feasibility of implementing the architecture using realXtend, an open platform for online multiuser virtual reality applications.
{"title":"A conceptual framework for middle-up-down semantic annotation of online 3D scenes","authors":"L. Tuovinen, Jari Kahelin, J. Röning","doi":"10.1109/ICOSC.2015.7050853","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050853","url":null,"abstract":"Semantic metadata has many uses in online virtual environments, but the technology for acquiring and managing metadata is not yet fully developed. This paper presents a partial solution that combines and extends traditional top-down and bottom-up approaches to semantic annotation. The paper proposes a metadata architecture composed of three layers, each of which has a metadata store and an interface for importing metadata from a specific group of providers. Under certain conditions, metadata can be propagated between layers, enabling it to be shared with other users of the same application or other applications based on the same virtual reality model. A proof-of-concept implementation demonstrates the feasibility of implementing the architecture using realXtend, an open platform for online multiuser virtual reality applications.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132945564","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050783
M. Sakurada, T. Yairi, Y. Nakajima, N. Nishimura, Devi Parikh
In this paper, we introduce a novel approach where the system involves human knowledge in the classification task using decision trees. Machine learning techniques are now applied to a variety of tasks in real-world problems. The computer performs complex computations better than humans. However, in many real-world applications, humans have background domain knowledge about the problem that the computer often does not have. For instance, in a spacecraft status classification task, humans have a sense for which factors are likely to correlate with the classes of interest. Without this knowledge, machines may overfit to training data. We propose to combine two models: one based on human reasoning, common sense, or heuristics, and the other learned by a machine learning algorithm in a data-driven manner. In our experiments, we use decision trees and categorical features so that the model consists of rules which are semantic and interpretable for humans. Our proposed approach results in an improvement in classification performance over either models alone. Our work illustrates the possibility of integrating human knowledge and artificial intelligence.
{"title":"Semantic classification of spacecraft's status: integrating system intelligence and human knowledge","authors":"M. Sakurada, T. Yairi, Y. Nakajima, N. Nishimura, Devi Parikh","doi":"10.1109/ICOSC.2015.7050783","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050783","url":null,"abstract":"In this paper, we introduce a novel approach where the system involves human knowledge in the classification task using decision trees. Machine learning techniques are now applied to a variety of tasks in real-world problems. The computer performs complex computations better than humans. However, in many real-world applications, humans have background domain knowledge about the problem that the computer often does not have. For instance, in a spacecraft status classification task, humans have a sense for which factors are likely to correlate with the classes of interest. Without this knowledge, machines may overfit to training data. We propose to combine two models: one based on human reasoning, common sense, or heuristics, and the other learned by a machine learning algorithm in a data-driven manner. In our experiments, we use decision trees and categorical features so that the model consists of rules which are semantic and interpretable for humans. Our proposed approach results in an improvement in classification performance over either models alone. Our work illustrates the possibility of integrating human knowledge and artificial intelligence.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128174627","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050823
Mariia Gavriushenko, M. Kankaanranta, P. Neittaanmäki
This article focuses on proposed semantically enhanced model of decision support system for learning management system (LMS). The model is based on a survey of LMSs and various plugins used in these to improve educational process. Systems based on semantic technologies are capable of integrating heterogeneous data, flexibly changing data schemas, semantic search (using ontologies), and joint knowledge development. The knowledge base that was developed for the proposed system model is presented in an ontological form. Ontology-based applications limit the "fragility" of the software and increase the likelihood of its reuse. In addition, they profitably redirect the efforts previously focused on software development and maintenance of creation and modification of knowledge structures. In the proposed knowledge base, we developed the necessary rules for further recommendations of specialization and courses for users. These recommendations are based on users' data extracted from profiles and user preferences.
{"title":"Semantically enhanced decision support for learning management systems","authors":"Mariia Gavriushenko, M. Kankaanranta, P. Neittaanmäki","doi":"10.1109/ICOSC.2015.7050823","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050823","url":null,"abstract":"This article focuses on proposed semantically enhanced model of decision support system for learning management system (LMS). The model is based on a survey of LMSs and various plugins used in these to improve educational process. Systems based on semantic technologies are capable of integrating heterogeneous data, flexibly changing data schemas, semantic search (using ontologies), and joint knowledge development. The knowledge base that was developed for the proposed system model is presented in an ontological form. Ontology-based applications limit the \"fragility\" of the software and increase the likelihood of its reuse. In addition, they profitably redirect the efforts previously focused on software development and maintenance of creation and modification of knowledge structures. In the proposed knowledge base, we developed the necessary rules for further recommendations of specialization and courses for users. These recommendations are based on users' data extracted from profiles and user preferences.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128268256","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050817
R. Saripalle, A. D. L. R. Algarin, Timoteus B. Ziminski
Information privacy and security plays a major role in domains where sensitive information is handled, such as case studies of rare diseases. Currently, security for accessing any sensitive information is provided by various mechanisms at the user/system level by employing access control models such as Role Based Access Control. However, these approaches leave security at the knowledge level unattended, which can be inadequate. For example, in healthcare, ontology-based information extraction is employed for extracting medical knowledge from sensitive structured/unstructured data sources. These information extraction systems act on sensitive data sources which are protected against unauthorized access at the system level based on the user, context and permissions, but the knowledge that can be extracted from these sources is not. In this paper we tackle the security or access control at the knowledge level by presenting a model, to enforce knowledge security/access by leveraging knowledge sources (currently focused on RDF) with the RBAC model. The developed model filters out knowledge by means of binary permissions on the knowledge source, providing each user with a different view of the knowledge source.
{"title":"Towards knowledge level privacy and security using RDF/RDFS and RBAC","authors":"R. Saripalle, A. D. L. R. Algarin, Timoteus B. Ziminski","doi":"10.1109/ICOSC.2015.7050817","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050817","url":null,"abstract":"Information privacy and security plays a major role in domains where sensitive information is handled, such as case studies of rare diseases. Currently, security for accessing any sensitive information is provided by various mechanisms at the user/system level by employing access control models such as Role Based Access Control. However, these approaches leave security at the knowledge level unattended, which can be inadequate. For example, in healthcare, ontology-based information extraction is employed for extracting medical knowledge from sensitive structured/unstructured data sources. These information extraction systems act on sensitive data sources which are protected against unauthorized access at the system level based on the user, context and permissions, but the knowledge that can be extracted from these sources is not. In this paper we tackle the security or access control at the knowledge level by presenting a model, to enforce knowledge security/access by leveraging knowledge sources (currently focused on RDF) with the RBAC model. The developed model filters out knowledge by means of binary permissions on the knowledge source, providing each user with a different view of the knowledge source.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126547342","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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