G. Andrienko, N. Andrienko, G. Fuchs, A. Raimond, J. Symanzik, Cezary Ziemlicki
Data reflecting movements of people, such as GPS or GSM tracks, can be a source of information about mobility behaviors and activities of people. Such information is required for various kinds of spatial planning in the public and business sectors. Movement data by themselves are semantically poor. Meaningful information can be derived by means of interactive visual analysis performed by a human expert; however, this is only possible for data about a small number of people. We suggest an approach that allows scaling to large datasets reflecting movements of numerous people. It includes extracting stops, clustering them for identifying personal places of interest (POIs), and creating temporal signatures of the POIs characterizing the temporal distribution of the stops with respect to the daily and weekly time cycles and the time line. The analyst can give meanings to selected POIs based on their temporal signatures (i.e., classify them as home, work, etc.), and then POIs with similar signatures can be classified automatically. We demonstrate the possibilities for interactive visual semantic analysis by example of GSM, GPS, and Twitter data. GPS data allow inferring richer semantic information, but temporal signatures alone may be insufficient for interpreting short stops. Twitter data are similar to GSM data but additionally contain message texts, which can help in place interpretation. We plan to develop an intelligent system that learns how to classify personal places and trips while a human analyst visually analyzes and semantically annotates selected subsets of movement data.
{"title":"Extracting Semantics of Individual Places from Movement Data by Analyzing Temporal Patterns of Visits","authors":"G. Andrienko, N. Andrienko, G. Fuchs, A. Raimond, J. Symanzik, Cezary Ziemlicki","doi":"10.1145/2534848.2534851","DOIUrl":"https://doi.org/10.1145/2534848.2534851","url":null,"abstract":"Data reflecting movements of people, such as GPS or GSM tracks, can be a source of information about mobility behaviors and activities of people. Such information is required for various kinds of spatial planning in the public and business sectors. Movement data by themselves are semantically poor. Meaningful information can be derived by means of interactive visual analysis performed by a human expert; however, this is only possible for data about a small number of people. We suggest an approach that allows scaling to large datasets reflecting movements of numerous people. It includes extracting stops, clustering them for identifying personal places of interest (POIs), and creating temporal signatures of the POIs characterizing the temporal distribution of the stops with respect to the daily and weekly time cycles and the time line. The analyst can give meanings to selected POIs based on their temporal signatures (i.e., classify them as home, work, etc.), and then POIs with similar signatures can be classified automatically. We demonstrate the possibilities for interactive visual semantic analysis by example of GSM, GPS, and Twitter data. GPS data allow inferring richer semantic information, but temporal signatures alone may be insufficient for interpreting short stops. Twitter data are similar to GSM data but additionally contain message texts, which can help in place interpretation. We plan to develop an intelligent system that learns how to classify personal places and trips while a human analyst visually analyzes and semantically annotates selected subsets of movement data.","PeriodicalId":41799,"journal":{"name":"Comparatist","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85782742","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}
Place narratives provide a rich resource of learning how humans localize places. Place localization can be done in various ways, relative to other spatial referents, and relative to agents and their activities in which these referents may be involved. How can we describe places based on their spatial and semantic relationships to objects, qualities, and activities? How can these relations help us improve automated localization of places implicit in textual descriptions? In this paper, we motivate research on extraction of semantic place localization statements from text corpora which can be used for improving document retrieval and for reconstructing locations. The idea is to combine Semantic Web reasoning with existing geographic information retrieval (GIR) and structural text extraction for this purpose. GIR and Semantic Web technology have matured during the last years, but still largely exist in parallel. Current localization approaches have been focusing on the extraction of unstructured word lists from texts, including toponyms and geographic features, not on human place descriptions on a sentence level.
{"title":"Semantic place localization from narratives","authors":"S. Scheider, R. Purves","doi":"10.1145/2534848.2534858","DOIUrl":"https://doi.org/10.1145/2534848.2534858","url":null,"abstract":"Place narratives provide a rich resource of learning how humans localize places. Place localization can be done in various ways, relative to other spatial referents, and relative to agents and their activities in which these referents may be involved. How can we describe places based on their spatial and semantic relationships to objects, qualities, and activities? How can these relations help us improve automated localization of places implicit in textual descriptions? In this paper, we motivate research on extraction of semantic place localization statements from text corpora which can be used for improving document retrieval and for reconstructing locations. The idea is to combine Semantic Web reasoning with existing geographic information retrieval (GIR) and structural text extraction for this purpose. GIR and Semantic Web technology have matured during the last years, but still largely exist in parallel. Current localization approaches have been focusing on the extraction of unstructured word lists from texts, including toponyms and geographic features, not on human place descriptions on a sentence level.","PeriodicalId":41799,"journal":{"name":"Comparatist","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90916853","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}
The diversity of ways in which toponyms are specified often results in mismatches between queries and the place names contained in gazetteers. Search terms that include unofficial variants of official place names, unanticipated transliterations, and typos are frequently similar but not identical to the place names contained in the gazetteer. String similarity measures can mitigate this problem, but given their task-dependent performance, the optimal choice of measure is unclear. We constructed a task in which place names had to be matched to variants of those names listed in the GEOnet Names Server, comparing 21 different measures on datasets containing romanized toponyms from 11 different countries. Best-performing measures varied widely across datasets, but were highly consistent within-country and within-language. We discuss which measures worked best for particular languages and provide recommendations for selecting appropriate string similarity measures.
{"title":"A Comparison of String Similarity Measures for Toponym Matching","authors":"Gabriel Recchia, M. Louwerse","doi":"10.1145/2534848.2534850","DOIUrl":"https://doi.org/10.1145/2534848.2534850","url":null,"abstract":"The diversity of ways in which toponyms are specified often results in mismatches between queries and the place names contained in gazetteers. Search terms that include unofficial variants of official place names, unanticipated transliterations, and typos are frequently similar but not identical to the place names contained in the gazetteer. String similarity measures can mitigate this problem, but given their task-dependent performance, the optimal choice of measure is unclear. We constructed a task in which place names had to be matched to variants of those names listed in the GEOnet Names Server, comparing 21 different measures on datasets containing romanized toponyms from 11 different countries. Best-performing measures varied widely across datasets, but were highly consistent within-country and within-language. We discuss which measures worked best for particular languages and provide recommendations for selecting appropriate string similarity measures.","PeriodicalId":41799,"journal":{"name":"Comparatist","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78608287","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}
A computational model of understanding place descriptions is a cardinal issue in multiple disciplines and provides critical applications especially in dialog-driven geolocation services. This research targets the automated extraction of spatial triplets to represent qualitative spatial relations between recognized places from natural language place descriptions via a simple class of locative expressions. We attempt to produce triplets, informative and convenient enough as a medium to convert verbal descriptions to graph representations of places and their relationships. We present a reasoning approach devoid of any external resources (such as maps, path geometries or robotic vision) for understanding place descriptions. We then apply our methodologies to situated place descriptions and study the results, its errors and implied future research.
{"title":"Extracting Spatial Information From Place Descriptions","authors":"Arbaz Khan, M. Vasardani, S. Winter","doi":"10.1145/2534848.2534857","DOIUrl":"https://doi.org/10.1145/2534848.2534857","url":null,"abstract":"A computational model of understanding place descriptions is a cardinal issue in multiple disciplines and provides critical applications especially in dialog-driven geolocation services. This research targets the automated extraction of spatial triplets to represent qualitative spatial relations between recognized places from natural language place descriptions via a simple class of locative expressions. We attempt to produce triplets, informative and convenient enough as a medium to convert verbal descriptions to graph representations of places and their relationships. We present a reasoning approach devoid of any external resources (such as maps, path geometries or robotic vision) for understanding place descriptions. We then apply our methodologies to situated place descriptions and study the results, its errors and implied future research.","PeriodicalId":41799,"journal":{"name":"Comparatist","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82282266","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}
Chen Zhong, Xianfeng Huang, S. Arisona, G. Schmitt
Identifying the spatial structure generated by urban movements contributes to a better understanding of urban dynamics and is crucial to urban planning applications. Despite a number of studies concerning functional urban space, related research is still in a development phase, especially using emerging urban movement data. This study proposes a centrality index and attractiveness indices for detecting the urban spatial structure of functional centers and their spatial impacts using transportation data. The basic idea of these indices is to build a relationship between the activity patterns (distribution, density, and diversity) and urban form. Accordingly, measurements, spatial analysis, and clustering methods are presented. Taking Singapore as a case study area, we applied the proposed indices and measurements to travel survey data of different years, through which centers of urban activities as well as the changing urban form are detected and compared quantitatively. Our approach yields important insights into urban phenomena generated by human movements. It represents a novel way of quantitative urban analysis and explicit urban change identification.
{"title":"Identifying Spatial Structure of Urban Functional Centers Using Travel Survey Data: A Case Study of Singapore","authors":"Chen Zhong, Xianfeng Huang, S. Arisona, G. Schmitt","doi":"10.1145/2534848.2534855","DOIUrl":"https://doi.org/10.1145/2534848.2534855","url":null,"abstract":"Identifying the spatial structure generated by urban movements contributes to a better understanding of urban dynamics and is crucial to urban planning applications. Despite a number of studies concerning functional urban space, related research is still in a development phase, especially using emerging urban movement data. This study proposes a centrality index and attractiveness indices for detecting the urban spatial structure of functional centers and their spatial impacts using transportation data. The basic idea of these indices is to build a relationship between the activity patterns (distribution, density, and diversity) and urban form. Accordingly, measurements, spatial analysis, and clustering methods are presented. Taking Singapore as a case study area, we applied the proposed indices and measurements to travel survey data of different years, through which centers of urban activities as well as the changing urban form are detected and compared quantitatively. Our approach yields important insights into urban phenomena generated by human movements. It represents a novel way of quantitative urban analysis and explicit urban change identification.","PeriodicalId":41799,"journal":{"name":"Comparatist","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90058955","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}
C. Eick, F. Akdag, Paul K. Amalaman, Aditya Tadakaluru
In general, it is desirable to have automatic tools that identify places in spatial data and to describe their characteristics, creating high-level summaries for spatial datasets which are valuable for planners, scientists, and policy makers. In this paper, we present a methodology that identifies a set of places based on a user-defined notion of interestingness and then identifies the scope of each place. A spatial clustering approach is used for the first step. For the second step, polygons are used as models to describe the scope of a place---the spatial area the place occupies. A 2-step methodology is introduced to compute a set of polygons for a set of places with each space being characterized by the set of objects which occupy the particular space. In the first step, an algorithm called LDTR is introduced that tightens the convex hull of a set of spatial objects by removing larger triangles of its Delaunay triangulation, obtaining an initial polygon for each place. Next, a post processing algorithm PolyRepair is introduced that tightens polygons further by reducing the overlap between the generated polygons; the algorithm gives preference to tightening polygons that have a lot of overlap with other polygons as the goal is to keep polygon tightening to a minimum. Finally, the two novel algorithms are demonstrated and evaluated for an urban computing benchmark.
{"title":"A Framework for Discriminative Polygonal Place Scoping","authors":"C. Eick, F. Akdag, Paul K. Amalaman, Aditya Tadakaluru","doi":"10.1145/2534848.2534849","DOIUrl":"https://doi.org/10.1145/2534848.2534849","url":null,"abstract":"In general, it is desirable to have automatic tools that identify places in spatial data and to describe their characteristics, creating high-level summaries for spatial datasets which are valuable for planners, scientists, and policy makers. In this paper, we present a methodology that identifies a set of places based on a user-defined notion of interestingness and then identifies the scope of each place. A spatial clustering approach is used for the first step. For the second step, polygons are used as models to describe the scope of a place---the spatial area the place occupies. A 2-step methodology is introduced to compute a set of polygons for a set of places with each space being characterized by the set of objects which occupy the particular space. In the first step, an algorithm called LDTR is introduced that tightens the convex hull of a set of spatial objects by removing larger triangles of its Delaunay triangulation, obtaining an initial polygon for each place. Next, a post processing algorithm PolyRepair is introduced that tightens polygons further by reducing the overlap between the generated polygons; the algorithm gives preference to tightening polygons that have a lot of overlap with other polygons as the goal is to keep polygon tightening to a minimum. Finally, the two novel algorithms are demonstrated and evaluated for an urban computing benchmark.","PeriodicalId":41799,"journal":{"name":"Comparatist","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89009980","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}
Klaas Jordan, Iaroslav Sheptykin, Barbara Grüter, Heide-Rose Vatterrott
The goal of this paper is to investigate the possibility to identify structural landmarks using movement data collected during an event of a location-based game. Landmarks are visually, structurally or cognitively salient, spatial features used for example for navigation purposes to situate and to orientate oneself within the own world and to locate proximate or distant objects or locations within this space. Structural salience is a characteristic of a landmark defined by the prominence of its spatial position. We use relations between movement and landmarks in order to reason about the structural significance of locations in a city park, based on the movement behavior exhibited by the players of the location-based game called Ostereiersuche. The results of this study suggest that structurally salient landmarks can be identified based on an analysis of movement events recorded in a location-based game. The introduced "player movement - landmark detection loop" represents a first instance of a landmark management system as one layer of a mobile game play ecosystem, the mobile game lab.
{"title":"Identification of structural landmarks in a park using movement data collected in a location-based game","authors":"Klaas Jordan, Iaroslav Sheptykin, Barbara Grüter, Heide-Rose Vatterrott","doi":"10.1145/2534848.2534853","DOIUrl":"https://doi.org/10.1145/2534848.2534853","url":null,"abstract":"The goal of this paper is to investigate the possibility to identify structural landmarks using movement data collected during an event of a location-based game. Landmarks are visually, structurally or cognitively salient, spatial features used for example for navigation purposes to situate and to orientate oneself within the own world and to locate proximate or distant objects or locations within this space. Structural salience is a characteristic of a landmark defined by the prominence of its spatial position. We use relations between movement and landmarks in order to reason about the structural significance of locations in a city park, based on the movement behavior exhibited by the players of the location-based game called Ostereiersuche. The results of this study suggest that structurally salient landmarks can be identified based on an analysis of movement events recorded in a location-based game. The introduced \"player movement - landmark detection loop\" represents a first instance of a landmark management system as one layer of a mobile game play ecosystem, the mobile game lab.","PeriodicalId":41799,"journal":{"name":"Comparatist","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78536547","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}
M. Shirai, Masaharu Hirota, H. Ishikawa, Shohei Yokoyama
Social media sites include many photographs taken at various locations and times. As described herein, we propose a method to identify hotspots to visualize user interest using geo-tagging of photographs posted on social media sites. Hotspots are classifiable to two types based on its locations: area of interest or shooting spot. In some cases, a hotspot has relation to other hotspots. We extract and classify hotspots according to that relation based on the bias of photograph location and photograph orientation. Moreover, we classify whether an event happened or did not happen in extracted hotspots.
{"title":"A method of Area of Interest and Shooting Spot Detection using Geo-tagged Photographs","authors":"M. Shirai, Masaharu Hirota, H. Ishikawa, Shohei Yokoyama","doi":"10.1145/2534848.2534854","DOIUrl":"https://doi.org/10.1145/2534848.2534854","url":null,"abstract":"Social media sites include many photographs taken at various locations and times. As described herein, we propose a method to identify hotspots to visualize user interest using geo-tagging of photographs posted on social media sites. Hotspots are classifiable to two types based on its locations: area of interest or shooting spot. In some cases, a hotspot has relation to other hotspots. We extract and classify hotspots according to that relation based on the bias of photograph location and photograph orientation. Moreover, we classify whether an event happened or did not happen in extracted hotspots.","PeriodicalId":41799,"journal":{"name":"Comparatist","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76099304","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}
Place-based GIS are still a novel research topic and break with some traditions of established systems. The typical spatial perspective is based on geometric reference systems that include coordinates, distances, topology, and directions; while the alternative platial perspective is usually characterized by place names and descriptions as well as semantic relationships between places. In past decades, space-based geographic information systems have made significant progress in terms of theories, models, functionalities, and applications. In contrast, place-based GIS are not yet well developed, although there is an increasing interest in platial and especially relational approaches. In this paper we take an example-driven, first step towards introducing place-based versions of the well known spatial join and buffer operations, and apply them to deal with place-based semantic compression and expansion in DBpedia.
{"title":"Towards Platial Joins and Buffers in Place-Based GIS","authors":"Song Gao, K. Janowicz, Grant McKenzie, Linna Li","doi":"10.1145/2534848.2534856","DOIUrl":"https://doi.org/10.1145/2534848.2534856","url":null,"abstract":"Place-based GIS are still a novel research topic and break with some traditions of established systems. The typical spatial perspective is based on geometric reference systems that include coordinates, distances, topology, and directions; while the alternative platial perspective is usually characterized by place names and descriptions as well as semantic relationships between places. In past decades, space-based geographic information systems have made significant progress in terms of theories, models, functionalities, and applications. In contrast, place-based GIS are not yet well developed, although there is an increasing interest in platial and especially relational approaches. In this paper we take an example-driven, first step towards introducing place-based versions of the well known spatial join and buffer operations, and apply them to deal with place-based semantic compression and expansion in DBpedia.","PeriodicalId":41799,"journal":{"name":"Comparatist","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73808785","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}
Lamia Belouaer, David Brosset, Christophe Claramunt
When maps are not available verbal route descriptions provide a useful alternative for humans navigating in a natural environment. The semantics that emerge from such descriptions encompass several modelling abstractions that have been long studied by spatial cognition. However, a formal representation of navigation descriptions still remains a research challenge. The objective of the research presented in this paper is to provide a modelling approach for the description and fusion of several verbal route descriptions, and to identify the relevant places that emerge. A semantic spatial network is derived, thus generating a conceptual map that might be used for pedestrian navigation. The semantic spatial network is generated after application of a genetic algorithm and fusion rules to verbal route descriptions recorded by several humans navigating in a given natural environment. Preliminary results are encouraging but still have to be compared with real maps and with expert knowledge.
{"title":"A rule-based genetic algorithm for mapping route descriptions towards map representations","authors":"Lamia Belouaer, David Brosset, Christophe Claramunt","doi":"10.1145/2534848.2534852","DOIUrl":"https://doi.org/10.1145/2534848.2534852","url":null,"abstract":"When maps are not available verbal route descriptions provide a useful alternative for humans navigating in a natural environment. The semantics that emerge from such descriptions encompass several modelling abstractions that have been long studied by spatial cognition. However, a formal representation of navigation descriptions still remains a research challenge. The objective of the research presented in this paper is to provide a modelling approach for the description and fusion of several verbal route descriptions, and to identify the relevant places that emerge. A semantic spatial network is derived, thus generating a conceptual map that might be used for pedestrian navigation. The semantic spatial network is generated after application of a genetic algorithm and fusion rules to verbal route descriptions recorded by several humans navigating in a given natural environment. Preliminary results are encouraging but still have to be compared with real maps and with expert knowledge.","PeriodicalId":41799,"journal":{"name":"Comparatist","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87111347","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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