Spatial signatures for geographic feature types: examining gazetteer ontologies using spatial statistics

Digital gazetteers play a key role in modern information systems and infrastructures. They facilitate (spatial) search, deliver contextual information to recommended systems, enrich textual information with geographical references, and provide stable identifiers to interlink actors, events, and objects by the places they interact with. Hence, it is unsurprising that gazetteers, such as GeoNames, are among the most densely interlinked hubs on the Web of Linked Data. A wide variety of digital gazetteers have been developed over the years to serve different communities and needs. These gazetteers differ in their overall coverage, underlying data sources, provided functionality, and geographic feature type ontologies. Consequently, place types that share a common name may differ substantially between gazetteers, whereas types labeled differently may, in fact, specify the same or similar places. This makes data integration and federated queries challenging, if not impossible. To further complicate the situation, most popular and widely adopted geo‐ontologies are lightweight and thus under‐specific to a degree where their alignment and matching become nothing more than educated guesses. The most promising approach to addressing this problem, and thereby enabling the meaningful integration of gazetteer data across feature types, seems to be a combination of top‐down knowledge representation with bottom‐up data‐driven techniques such as feature engineering and machine learning. In this work, we propose to derive indicative spatial signatures for geographic feature types by using spatial statistics. We discuss how to create such signatures by feature engineering and demonstrate how the signatures can be applied to better understand the differences and commonalities of three major gazetteers, namely DBpedia Places, GeoNames, and TGN.     

Wāhi,a discrete global grid gazetteer built using linked open data

ABSTRACT

Discrete global grid systems have become an important component of Digital Earth systems. However, previously there has not existed an easy way to map between named places (toponyms) and the cells of a discrete global grid system. The lack of such a tool has limited the opportunities to synthesize social place-based data with the more standard Earth and environmental science data currently being analyzed in Digital Earth applications. This paper introduces Wāhi, the first gazetteer to map entities from the GeoNames database to multiple discrete global grid systems. A gazetteer service is presented that exposes the grid system and the associated gazetteer data as Linked Data. A set of use cases for the discrete global grid gazetteer is discussed.     

A spatially explicit reinforcement learning model for geographic knowledge graph summarization

Web‐scale knowledge graphs such as the global Linked Data cloud consist of billions of individual statements about millions of entities. In recent years, this has fueled the interest in knowledge graph summarization techniques that compute representative subgraphs for a given collection of nodes. In addition, many of the most densely connected entities in knowledge graphs are places and regions, often characterized by thousands of incoming and outgoing relationships to other places, actors, events, and objects. In this article, we propose a novel summarization method that incorporates spatially explicit components into a reinforcement learning framework in order to help summarize geographic knowledge graphs, a topic that has not been considered in previous work. Our model considers the intrinsic graph structure as well as the extrinsic information to gain a more comprehensive and holistic view of the summarization task. By collecting a standard data set and evaluating our proposed models, we demonstrate that the spatially explicit model yields better results than non‐spatial models, thereby demonstrating that spatial is indeed special as far as summarization is concerned.     

Computing and querying strict,approximate, and metrically refined topological relations in linked geographic data

Geographic entities and the information associated with them play a major role in Web‐scale knowledge graphs such as Linked Data. Interestingly, almost all major datasets represent places and even entire regions as point coordinates. There are two key reasons for this. First, complex geometries are difficult to store and query using the current Linked Data technology stack to a degree where many queries take minutes to return or will simply time out. Second, the absence of complex geometries confirms a common suspicion among GIScientists, namely that for many everyday queries place‐based relational knowledge is more relevant than raw geometries alone. To give an illustrative example, the statement that the White House is in Washington, DC is more important for gaining an understating of the city than the exact geometries of both entities. This does not imply that complex geometries are unimportant but that (topological) relations should also be extracted from them. As Egenhofer and Mark (1995b) put it in their landmark paper on naive geography, topology matters, metric refines. In this work we demonstrate how to compute and utilize strict, approximate, and metrically refined topological relations between several geographic feature types in DBpedia and compare our results to approaches that compute result sets for topological queries on the fly.     

Semantic Expansion of Geographic Web Queries Based on Natural Language Positioning Expressions

The need for better Web search tools is getting increasing attention nowadays. About 20% of the queries currently submitted to search engines include geographic references. Thus, it is particularly important to work with the semantics of such queries, both by understanding the terminology and by recognizing geographic references in natural language text. In this paper, we explore the use of natural language expressions, which we call positioning expressions, to perform geographic searches on the Web, without resorting to geocoded data or gazetteers. Such positioning expressions denote the location of a subject of interest with respect to a landmark. Our approach leads to a query expansion technique that can be explored by virtually any keyword‐based search engine. Results obtained in our experiments show an expressive improvement over the traditional keyword‐based search and a potential path for tackling many kinds of common geographic queries.     

Deeply integrating Linked Data with Geographic Information Systems

The realization that knowledge often forms a densely interconnected graph has fueled the development of graph databases, Web‐scale knowledge graphs and query languages for them, novel visualization and query paradigms, as well as new machine learning methods tailored to graphs as data structures. One such example is the densely connected and global Linked Data cloud that contains billions of statements about numerous domains, including life science and geography. While Linked Data has found its way into everyday applications such as search engines and question answering systems, there is a growing disconnect between the classical ways in which Geographic Information Systems (GIS) are still used today and the open‐ended, exploratory approaches used to retrieve and consume data from knowledge graphs such as Linked Data. In this work, we conceptualize and prototypically implement a Linked Data connector framework as a set of toolboxes for Esri's ArcGIS to close this gap and enable the retrieval, integration, and analysis of Linked Data from within GIS. We discuss how to connect to Linked Data endpoints, how to use ontologies to probe data and derive appropriate GIS representations on the fly, how to make use of reasoning, how to derive data that are ready for spatial analysis out of RDF triples, and, most importantly, how to utilize the link structure of Linked Data to enable analysis. The proposed Linked Data connector framework can also be regarded as the first step toward a guided geographic question answering system over geographic knowledge graphs.     

基于GeoNames和Solr的地名数据全文检索

地名数据是一种重要的地理信息资源,目前我国的地名数据库建设多局限于国内地名,缺少国外数据。传统地名数据的检索多为关键字查询,查询效率低且无法用于复杂地名查询。文中提出一种基于开源地名数据库GeoNames和开源搜索引擎Solr的地名数据全文检索方法,通过分析GeoNames的数据类型和结构,构建MySQL地名数据库,并利用Solr建立地名索引,提供基于Web服务的地名数据全文检索。实验表明,基于Solr的地名数据全文检索能够显著提高地名数据检索效率,对于复杂地名查询,也能进行有效地检索。     

A framework for connecting two interoperability universes: OGC Web Feature Services and Linked Data

Diverse studies have shown that about 80% of all available data are related to a spatial location. Most of these geospatial data are available as structured and semi‐structured datasets, and often use distinct data models, are encoded using ad‐hoc vocabularies, and sometimes are being published in non‐standard formats. Hence, these data are isolated within silos and cannot be shared and integrated across organizations and communities. Spatial Data Infrastructures (SDIs) have emerged and contributed to significantly enhance data discovery and accessibility based on OGC (Open Geospatial Consortium) Web services. However, finding, accessing, and using data disseminated through SDIs are still difficult for non‐expert users. Overcoming the current geospatial data challenges involves adopting the best practices to expose, share, and integrate data on the Web, that is, Linked Data. In this article, we have developed a framework for generating, enriching, and exploiting geospatial Linked Data from multiple and heterogeneous geospatial data sources. This proposal allows connecting two interoperability universes (SDIs, more specifically Web Feature Services, WFS, and Semantic Web technologies), which is evaluated through a study case in the (geo)biodiversity domain.     

A weighted multi-attribute method for matching user-generated Points of Interest

To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multi-dimensional data sources and not merely its volume. To fully exploit this variety, however, requires conflation. This is a two-step process. First, one has to establish identity relations between information entities across different data sources; and second, attribute values have to be merged according to certain procedures that avoid logical contradictions. The first step, also called matching, can be thought of as a weighted combination of common attributes according to some similarity measures. In this work, we propose such a matching based on multiple attributes of Points of Interest (POI) from the Location-based Social Network Foursquare and the local directory service Yelp. While both contain overlapping attributes that can be used for matching, they have specific strengths and weaknesses that make their conflation desirable. For instance, Foursquare offers information about user check-ins to places, while Yelp specializes in user-contributed reviews. We present a weighted multi-attribute matching strategy, evaluate its performance, and discuss application areas that benefit from a successful matching. Finally, we also outline how the established POI matches can be stored as Linked Data on the Semantic Web. Our strategy can automatically match 97% of randomly selected Yelp POI to their corresponding Foursquare entities.     

City model enrichment

The combination of mobile communication technology with location and orientation aware digital cameras has introduced increasing interest in the exploitation of 3D city models for applications such as augmented reality and automated image captioning. The effectiveness of such applications is, at present, severely limited by the often poor quality of semantic annotation of the 3D models. In this paper, we show how freely available sources of georeferenced Web 2.0 information can be used for automated enrichment of 3D city models. Point referenced names of prominent buildings and landmarks mined from Wikipedia articles and from the OpenStreetMaps digital map and Geonames gazetteer have been matched to the 2D ground plan geometry of a 3D city model. In order to address the ambiguities that arise in the associations between these sources and the city model, we present procedures to merge potentially related buildings and implement fuzzy matching between reference points and building polygons. An experimental evaluation demonstrates the effectiveness of the presented methods.     

地名本体的概念框架与构成研究

在提出地名本体的基本概念之后,根据基于地理空间语义网的日常地理信息查询需要,进行了地名本体的概念设计,提出了通过复用地名词典和地理主题词表构建地名本体的概念框架和设计方法;提出地名本体由地理实体本体、实体类型本体和空间关系本体3种地理本体构成,并详细介绍了其设计结构。     

Using the Newly‐created ILE DBMS to Better Represent Temporal and Historical GIS Data

This article introduces a type of DBMS called the Intentionally‐Linked Entities (ILE) DBMS for use as the basis for temporal and historical Geographical Information Systems. ILE represents each entity in a database only once, thereby mostly eliminating redundancy and fragmentation, two major problems in Relational and other database systems. These advantages of ILE are realized by using relationship objects and pointers to implement all of the relationships among data entities in a native fashion using dynamically‐allocated linked data structures. ILE can be considered to be a modern and extended implementation of the E/R data model. ILE also facilitates storage of things that are more faithful to the historical records, such as gazetteer entries of places with imprecisely known or unknown locations. This is difficult in Relational database systems but is a routine task using ILE because ILE is implemented using modern memory allocation techniques. We use the China Historical GIS (CHGIS) and other databases to illustrate the advantages of ILE. This is accomplished by modeling these databases in ILE and comparing them to the existing Relational implementations.     

A RESTful proxy and data model for linked sensor data

Abstract

The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration. So far, sensor data are stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies toward their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (1) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (2) how to establish meaningful links between those data sets, that is, observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGC's Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.     

Evaluating Topological Queries in Linked Data Using DBpedia and GeoNames in Switzerland and Scotland

This article explores the practicability and assesses the quality of searches for Wikipedia pages of topologically related administrative divisions in Switzerland and Scotland via Linked Data. To this end, the quality of searches in the English and German versions of DBpedia is compared, as is that of searches in GeoNames and DBpedia using DBpedia's links to GeoNames or a manually created list of links. In addition, the effects of updates to Swiss municipality borders in the English version of DBpedia on standard quality metrics are studied. It turns out that live searches are practicable with acceptable performance, but that the quality of searches in terms of recall and precision is acceptable only if DBpedia is queried via GeoNames and DBpedia's links to GeoNames are replaced by manually created links. Searches for Scottish unitary authority wards perform poorly for all approaches investigated. Recall and precision are strongly influenced by changes in municipality borders, with a relatively long latency suggesting that, at least in some cases, administrative changes propagate slowly into DBpedia. These results have important implications for those seeking to use location as a means to link data.     

Cesium框架多源电子地图瓦片数据混搭方案设计

针对现有的商业地理信息系统平台难以灵活整合多源影像数据融合使用的问题,深入研究Cesium平台下多种数据源的空间参考差异和瓦片组织规则,针对不同空间参考的瓦片数据源进行混搭设计,提出基于Cesium的多源本地瓦片数据静态和动态加载方案。实验结果表明,单一瓦片数据源加载方案、静态和动态多源瓦片混搭方案的展示效果较好;静态瓦片混搭方案在加载不同空间参考瓦片数据时容易造成变形;动态多源、多空间参考瓦片混搭方案能有效克服变形问题且展示效果佳。本文实现了在开源GIS平台下的多源影像数据的融合使用,为地理信息系统与遥感类项目应用提供了一个低成本、高灵活性的解决方案,具有较为明显的实际应用价值。     

Exploring and Visualizing Differences in Geographic and Linguistic Web Coverage

This article reports on a study performed to understand the geographic and linguistic coverage of web resources, focusing on the example of tourism‐related themes in Switzerland. Search engine queries of web documents were used to gather counts for phrases in four different languages. The study focused on selected populated places and tourist attractions in Switzerland from three gazetteer datasets: topographic gazetteer data from the Swiss national mapping agency (SwissTopo); POI data from a commercial data provider (Tele Atlas) and user generated geographic content (geonames.org). The web counts illustrate the geographic extent and trends of web coverage of tourism for different languages. Results show that coverage for local languages, i.e. German, French and Italian, is more strongly related to the region of the spoken language. Correlation of the web counts to typical tourism indicators, e.g. population and number of hotel nights rented per year, are also computed and compared.     

A New Method for the Specification of Geographic Footprints in Digital Gazetteers

This paper presents the strategy used to add neighborhood names and footprints to the Los Angeles Digital Gazetteer. The gazetteer database currently contains 4,500 features and is needed to: (1) facilitate the specification of geographic footprints in the Qualified Dublin Core metadata records that are used to describe digital assets; and (2) support the search for and retrieval of selected objects based on location, time, format, and/or keyword. The role of the digital gazetteer and a new browser which will offer the library patron a web-based query form with an interactive map is explained. The interface can be used to draw a query on a map, and it provides a series of pull down menus that can be used to specify time periods, formats, collections, and key words of interest. A new method for specifying neighborhood footprints in the digital gazetteer is described in some detail, and opportunities are highlighted for generalizing the method to help with search and retrieval using the map browser.     

GeoTxt: A scalable geoparsing system for unstructured text geolocation

In this article we present GeoTxt, a scalable geoparsing system for the recognition and geolocation of place names in unstructured text. GeoTxt offers six named entity recognition (NER) algorithms for place name recognition, and utilizes an enterprise search engine for the indexing, ranking, and retrieval of toponyms, enabling scalable geoparsing for streaming text. GeoTxt offers a flexible application programming interface (API), allowing for customized attribute and/or spatial ranking of retrieved toponyms. We evaluate the system on a corpus of manually geo‐annotated tweets. First, we benchmark the performance of the six NERs that GeoTxt provides access to. Second, we assess GeoTxt toponym resolution accuracy incrementally, demonstrating improvements in toponym resolution achieved (or not achieved) by adding specific heuristics and disambiguation methods. Compared to using the GeoNames web service, GeoTxt's toponym resolution demonstrates a 20% accuracy gain. Our results show that places mentioned in the same tweet do not tend to be geographically proximate.     

Designing Service Architectures for Distributed Geoprocessing: Challenges and Future Directions

In this paper we study the feasibility of using services offered by a Spatial Data Infrastructure as a basis for distributed service oriented geoprocessing. By developing a prototype we demonstrate that a Spatial Data Infrastructure facilitates rapid development of applications that solve typical problems for an existing risk management application. The prototype provides users with a distributed application that enables the assessment of fire damage areas based on land cover data in a given area. The services involved in the application include: Web Feature Services, Web Map Services, a Gazetteer Service, a Catalogue Service, and Geoprocessing Services. We present the architecture of the application and describe details about implementation‐specific issues. We conclude that current OGC specifications provide a sound basis for developing service oriented architectures for geographic applications; however, in particular for geoprocessing applications, we question the feasibility of the use of Web Feature Services as data sources for larger amounts of data and call for further research in this direction.