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- item-1 hasContent miriam-baglioni.
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- item-2 hasContent emiliano-giovannetti.
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- item-3 hasContent maria-masserotti.
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- item-5 hasContent laura-spinsanti.
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- 6 creator christine-parent.
- 6 creator jose-macedo.
- 6 creator stefano-spaccapietra.
- 6 creator zhixian-yan.
- 6 type InProceedings.
- 6 label "Trajectory Ontologies".
- 6 sameAs 6.
- 6 abstract "Trajectory data play an important role in analyzing real world applications that involve with movement features, especially those complex natural and social phenomena such as \emph{bird migration}, \emph{transportation management}, \emph{urban planning} and \emph{tourism}. Traditional trajectory modeling mostly focuses on the geometric information, ignoring the concealed semantic geographic and domain knowledge. As an explicit specification of a shared conceptualization, ontology becomes a popular model to provide formal data semantics for information sharing and reusing. By means of designing a set of ontologies, this paper explores a semantic methodology for modeling and querying trajectory data, to extract the hidden geographic and domain knowledge. The definition of trajectory ontologies can be given at different abstraction levels, including domain-independent geometric ones and application-specific semantic ones. In addition, we propose an evaluation framework to verify the practicability and usability of the proposed trajectory ontologies.".
- 6 hasAuthorList authorList.
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- 6 keyword "spatiotemporal".
- 6 keyword "trajectory ontology".
- 6 title "Trajectory Ontologies".
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- item-1 hasContent zhixian-yan.
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- item-2 hasContent jose-macedo.
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- 7 creator alejandro-castaneda.
- 7 creator nicholas-del-rio.
- 7 creator paulo-pinheiro-da-silva.
- 7 creator vladik-kreinovich.
- 7 type InProceedings.
- 7 label "TrustMap: Trust Recommendations for Maps".
- 7 sameAs 7.
- 7 abstract "The web is a rich environment for exchanging spatial information. When spatial information is shared in the form of images, i.e., maps, these images almost never come with meta-information about how they were generated. This kind of meta-information is often called knowledge provenance and access to this information may facilitate users to make informed decisions about the quality of maps. In this paper, we propose TrustMap, a new approach for enhancing maps with trust recommendations. For a given map, TrustMap can generate recommendations from the knowledge provenance and a network of trust relations between sources of information used to derive the map. The paper describes a TrustMap implementation as well as a qualitative evaluation of the current implementation.".
- 7 hasAuthorList authorList.
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- 7 keyword "map understanding".
- 7 keyword "provenance".
- 7 keyword "recommendation".
- 7 keyword "trust".
- 7 title "TrustMap: Trust Recommendations for Maps".
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- item-1 hasContent paulo-pinheiro-da-silva.
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- item-2 hasContent nicholas-del-rio.
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- item-4 hasContent alejandro-castaneda.
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- 8 creator hiroaki-uchiyama.
- 8 creator lemonia-ragia.
- 8 creator michalis-foukarakis.
- 8 creator stavros-christodoulakis.
- 8 creator takuya-imai.
- 8 type InProceedings.
- 8 label "Geospatial Semantic Context Capturing for Picture Annotation, Retrieval and Visualization in SPIM".
- 8 sameAs 8.
- 8 abstract "Cameras can be very powerful sensors, enabling new user interaction metaphors and functionalities. They are not independent devices anymore and they can interoperate with external resources and services like user personal libraries, GIS systems, semantic GIS applications, semantic RFIDs, digital libraries, etc. In this paper, we present a Framework for the management of Semantic image information related to geospatial context and a system that has been built based on this Framework. Digital picture contents in the digital library can be interrelated with, and understood based on, contextual semantic geospatial information in the form of ontologies that the system manages. We describe the integrated camera system, the calculation and visualization of the contextual parameters, and the management of the semantics of the spatial objects. Finally, we present the implementation of SPIM, a picture management system that exploits spatial semantic contextual knowledge, its retrieval functionality and the visual user interfaces.".