摘要 :
Systematic Brain Informatics (BI) depends on a lot of prior knowledge, from experimental design to result interpretation. Scientific literatures are a kind of important knowledge source. However, it is difficult for researchers to...
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Systematic Brain Informatics (BI) depends on a lot of prior knowledge, from experimental design to result interpretation. Scientific literatures are a kind of important knowledge source. However, it is difficult for researchers to find really useful references from a large number of literatures. This paper proposes a personalized method of literature recommendation based on BI provenances. By adopting the interest retention model, user models can be built based on the Data-Brain and BI provenances. Furthermore, semantic similarity is added into traditional literature vector modeling for obtaining literature models. By measuring similarity between the user models and literature models, the really needed literatures can be obtained. Results of experiments show that the proposed method can effectively realize a personalized literature recommendation according to BI researchers' interests.
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摘要 :
Systematic Brain Informatics (BI) depends on a lot of prior knowledge, from experimental design to result interpretation. Scientific literatures are a kind of important knowledge source. However, it is difficult for researchers to...
展开
Systematic Brain Informatics (BI) depends on a lot of prior knowledge, from experimental design to result interpretation. Scientific literatures are a kind of important knowledge source. However, it is difficult for researchers to find really useful references from a large number of literatures. This paper proposes a personalized method of literature recommendation based on BI provenances. By adopting the interest retention model, user models can be built based on the Data-Brain and BI provenances. Furthermore, semantic similarity is added into traditional literature vector modeling for obtaining literature models. By measuring similarity between the user models and literature models, the really needed literatures can be obtained. Results of experiments show that the proposed method can effectively realize a personalized literature recommendation according to BI researchers' interests.
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摘要 :
Personalized knowledge recommendation is an effective measure to provide individual information services in the field of brain science. It is essential that a complete understanding of authors' interests and accurate recommendatio...
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Personalized knowledge recommendation is an effective measure to provide individual information services in the field of brain science. It is essential that a complete understanding of authors' interests and accurate recommendation are carried out to achieve this goal. In this paper, a collaborative recommendation method based on co-authorship is proposed to make. In our approach, analysis of collaborators' interests and the calculation of collaborative value are used for recommendations. Finally, the experiments using real documents associated with brain science are given and provide supports for collaborative document recommendation in the field of brain science.
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摘要 :
Personalized knowledge recommendation is an effective measure to provide individual information services in the field of brain science. It is essential that a complete understanding of authors' interests and accurate recommendatio...
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Personalized knowledge recommendation is an effective measure to provide individual information services in the field of brain science. It is essential that a complete understanding of authors' interests and accurate recommendation are carried out to achieve this goal. In this paper, a collaborative recommendation method based on co-authorship is proposed to make. In our approach, analysis of collaborators' interests and the calculation of collaborative value are used for recommendations. Finally, the experiments using real documents associated with brain science are given and provide supports for collaborative document recommendation in the field of brain science.
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摘要 :
Integrating brain big data is an important issue of the systematic Brain Informatics study. Provenances provide a practical approach to realize the information-level data integration. However, the existing neuroimaging provenances...
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Integrating brain big data is an important issue of the systematic Brain Informatics study. Provenances provide a practical approach to realize the information-level data integration. However, the existing neuroimaging provenances focus on describing experimental conditions and analytical processes, and cannot meet the requirement of integrating brain big data. This paper puts forward a provenance model of brain data, in which model elements are identified and defined by extending the Open Provenance Model. A case study is also described to demonstrate significance and usefulness of the proposed model. Such a provenance model facilitates more accurate modeling of brain data, including data creation and data processing for integrating various primitive brain data, brain data related information during the systematic Brain Informatics study.
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摘要 :
Integrating brain big data is an important issue of the systematic Brain Informatics study. Provenances provide a practical approach to realize the information-level data integration. However, the existing neuroimaging provenances...
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Integrating brain big data is an important issue of the systematic Brain Informatics study. Provenances provide a practical approach to realize the information-level data integration. However, the existing neuroimaging provenances focus on describing experimental conditions and analytical processes, and cannot meet the requirement of integrating brain big data. This paper puts forward a provenance model of brain data, in which model elements are identified and defined by extending the Open Provenance Model. A case study is also described to demonstrate significance and usefulness of the proposed model. Such a provenance model facilitates more accurate modeling of brain data, including data creation and data processing for integrating various primitive brain data, brain data related information during the systematic Brain Informatics study.
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摘要 :
Integrating brain big data is an important issue of the systematic Brain Informatics study. Provenances provide a practical approach to realize the information-level data integration. However, the existing neuroimaging provenances...
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Integrating brain big data is an important issue of the systematic Brain Informatics study. Provenances provide a practical approach to realize the information-level data integration. However, the existing neuroimaging provenances focus on describing experimental conditions and analytical processes, and cannot meet the requirement of integrating brain big data. This paper puts forward a provenance model of brain data, in which model elements are identified and defined by extending the Open Provenance Model. A case study is also described to demonstrate significance and usefulness of the proposed model. Such a provenance model facilitates more accurate modeling of brain data, including data creation and data processing for integrating various primitive brain data, brain data related information during the systematic Brain Informatics study.
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摘要 :
The documents selection related brain information based on the data-brain ontology not only has an important significance in the promotion of data-brain ontology, but also lays the foundation for knowledge integration. However, tr...
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The documents selection related brain information based on the data-brain ontology not only has an important significance in the promotion of data-brain ontology, but also lays the foundation for knowledge integration. However, traditional research of documents selection focuses on the concept, and cannot meet the requirement of the systematic Brain Informatics study. This paper analyzes the characteristics of source knowledge firstly with concepts, attributes and relations. Then, we calculate the weight of documents by using the improved method of VSM. Finally, the experiments using real documents associated with brain science are given and calculating the weight of each document achieves a better effect of ranking selection.
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摘要 :
The documents selection related brain information based on the data-brain ontology not only has an important significance in the promotion of data-brain ontology, but also lays the foundation for knowledge integration. However, tr...
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The documents selection related brain information based on the data-brain ontology not only has an important significance in the promotion of data-brain ontology, but also lays the foundation for knowledge integration. However, traditional research of documents selection focuses on the concept, and cannot meet the requirement of the systematic Brain Informatics study. This paper analyzes the characteristics of source knowledge firstly with concepts, attributes and relations. Then, we calculate the weight of documents by using the improved method of VSM. Finally, the experiments using real documents associated with brain science are given and calculating the weight of each document achieves a better effect of ranking selection.
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摘要 :
Finding and learning related research is a necessary work in Brain Informatics studies. However, the keyword-based search on brain and mental big data center often brings a large amount of unnecessary results. It is very difficult...
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Finding and learning related research is a necessary work in Brain Informatics studies. However, the keyword-based search on brain and mental big data center often brings a large amount of unnecessary results. It is very difficult to find needed research from those results for researchers. This paper proposes a Brain Informatics research recommendation system based on the Data-Brain and BI provenances. By choosing interest aspects from the Data-Brain and applying the unification of search and reasoning based on DatarBrain interests, the more accurate search can be realized to find really related literatures for supporting systematic Brain Informatics studies.
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