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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
143 541-555 Species denotes COVID-19 virus Tax:2697049
152 2624-2630 Species denotes people Tax:9606
153 932-940 Disease denotes COVID-19 MESH:C000657245
154 1105-1114 Disease denotes infection MESH:D007239
155 1311-1320 Disease denotes infection MESH:D007239
156 1591-1600 Disease denotes infection MESH:D007239
157 2210-2218 Disease denotes COVID-19 MESH:C000657245
158 2761-2770 Disease denotes infection MESH:D007239
159 2839-2847 Disease denotes COVID-19 MESH:C000657245
161 3719-3728 Disease denotes elevators MESH:D006973
163 5272-5278 Gene denotes beacon Gene:59286

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T107 0-4 Sentence denotes 2.3.
T108 5-45 Sentence denotes Interior Space Modelling Using IndoorGML
T109 46-103 Sentence denotes Indoor spaces differ from outdoor spaces in many aspects.
T110 104-256 Sentence denotes Basic concepts, data models, and standards of spatial information need to be redefined in order to meet the requirements of indoor spatial applications.
T111 257-370 Sentence denotes The proper representation of indoor spaces is a key issue for indoor spatial information modelling and analytics.
T112 371-525 Sentence denotes In recent years, the topic of 3D geospatial indoor modelling has been the focus of attention for location-based services and indoor navigation [42,43,44].
T113 526-836 Sentence denotes As the risk of COVID-19 virus transmission is higher in indoor environments, indoor space modelling is an important topic that facilitates the interoperability between different indoor and outdoor data collection methods and builds a consistent framework for collaborative research and development of the IoCT.
T114 837-972 Sentence denotes Aggregating different sensor observations for each room is essential for estimating the room’s COVID-19 risk and cleaning requirements.
T115 973-1070 Sentence denotes The visualization of Interior Space Risk State is another task which requires interior modelling.
T116 1071-1298 Sentence denotes In order to represent the risk of infection and to identify which specific areas of a building require the most cleaning, the status of individual floors should be able to be viewed separately from other floors in the building.
T117 1299-1485 Sentence denotes The risk of infection for various parts of each floor should be represented in a map with different ways of representing the data that is necessary for determining the risk in each area.
T118 1486-1705 Sentence denotes Moreover, in order to visualize trajectories for contact tracing and to quickly identify the location of infection spreading behavior within an indoor space, the buildings should be visible as a 3D construct on the map.
T119 1706-2060 Sentence denotes Therefore, in order to analyze the IoCT multi-sensors system and visualize it in indoor scenarios, an interoperable 3D building modelling standard, such as the “CityGML” Level of Detail 4 [45], “OGC IndoorGML” [46], building construction standards (e.g., “Building Information Modelling” (BIM), or “Industry Foundation Classes” (IFC) [47]), is necessary.
T120 2061-2145 Sentence denotes The main concern for using those models is their fit and how often they are updated.
T121 2146-2249 Sentence denotes Construction features of indoor spaces are not a major focus of COVID-19 workplace reopening scenarios.
T122 2250-2387 Sentence denotes Instead, the aggregation of sensors in each room, and the connectivity between the rooms, is fundamental for risk assessment and tracing.
T123 2388-2450 Sentence denotes Thus, the OGC IndoorGML is used for the IoCT indoor modelling.
T124 2451-2648 Sentence denotes According to Ryoo et al. [43], the OGC IndoorGML can be used more effectively than CityGML or any other geometric representations of space for analyzing the trajectories of people inside buildings.
T125 2649-2787 Sentence denotes This allows for more accurate appraisal of the types of intersection of trajectories, contact, and exposure for infection risk evaluation.
T126 2788-3042 Sentence denotes Applications such as cleaning risk assessments for COVID-19 workplace reopenings that need to operate efficiently together with indoor scales, various sensors, and objects that are moving and changing over time would benefit from using the OGC IndoorGML.
T127 3043-3201 Sentence denotes 3D geometry can be included in an IndoorGML document, and the overlap with other standards (e.g., OGC CityGML) can be addressed by adding external references.
T128 3202-3335 Sentence denotes There were no specific standards in the field of indoor geospatial modelling until the OGC standard IndoorGML was introduced in 2014.
T129 3336-3497 Sentence denotes The OGC IndoorGML intentionally focused on modelling indoor spaces using connected dual graphs for navigation purposes whilst considering various semantics [46].
T130 3498-3630 Sentence denotes OGC IndoorGML standard specifies an open data model and Extensible Markup Language (XML) schema for indoor spatial information [46].
T131 3631-3769 Sentence denotes Indoor space is comprised of connected constructs such as rooms, corridors, stairs, and elevators, all of which can be considered “Cells”.
T132 3770-3958 Sentence denotes This sets it apart from other standards in the field of 3D modelling, such as CityGML or IFC, as they model the building features (e.g., walls, windows) instead of the indoor space itself.
T133 3959-4033 Sentence denotes They also do not consider the connectivity and semantics of indoor spaces.
T134 4034-4209 Sentence denotes As shown in Figure 3, the nodes of the IndoorGML graph in this paper are considered the smallest organizational or structural units for the building and are called Cells [19].
T135 4210-4325 Sentence denotes Every Cell has an identifier (e.g., room number) and a location (x, y, z) to provide more precise location details.
T136 4326-4573 Sentence denotes Cells are connected and have a common boundary with other cells but do not overlap with them. “Geometric” features and “Topological” relationships, such as adjacency and connectivity, amongst indoor cells can be defined by an IndoorGML graph [48].
T137 4574-4794 Sentence denotes The topological relationships in IndoorGML are explicitly described using the xlink concept of XML provided by Geography Markup Language (GML) and the referencing is realized with the use of href attributes (xlink:href).
T138 4795-4962 Sentence denotes Semantics are also an important characteristic of the Cells in the IndoorGML. “Semantics” allow us to define cells which can be important for cleaning risk assessment.
T139 4963-5074 Sentence denotes For example, the most commonly used areas are public rooms, corridors, and doors, and thus present higher risk.
T140 5075-5196 Sentence denotes For this paper, an indoor space is represented as a topographic cellular space comprised of rooms, corridors, and stairs.
T141 5197-5304 Sentence denotes At the same time, it is also represented as different cellular spaces with beacon or camera coverage Cells.
T142 5305-5480 Sentence denotes Each semantic interpretation layer creates a different indoor model, and each model forms a separate dual graph layer (e.g., connectivity, sensor) for the same cellular space.
T143 5481-5603 Sentence denotes This multi-layered space model (Figure 3), is an aggregation of the space layers and inter-layer connections or relations.
T144 5604-5692 Sentence denotes The Indoor GML for the implementation of the structure space model is shown in Figure 4.