A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.     

顾及字符特征的中文地名词典查询方法

地名词典查询是地名校正、地名匹配等地名服务应用的重要基础,但是地名数量的快速增长使得词典查询性能面临严峻挑战。针对大规模数据环境中传统词典查询方法准确率不高且效率较低等问题,提出了一种顾及字符特征的中文地名词典查询方法（CGQM）。首先,查询具有相同字符特征的地名形成候选地名集合,同时构建单字索引提升查询效率;其次,依据字符数量特征比较查询地名与候选地名的差异,进一步过滤候选地名集合;最后,基于字符位置特征优化查询结果排序策略,使得结果排序更为合理。实验以全国地名词典为例,构建5组测试集进行CGQM方法与Lucene检索方法的对比分析。研究结果表明,CGQM方法对于增强地名词典查询功能、提升查询效率具有实际意义。     

旅游地文本中的细粒度地名可视化方法

在旅游文本中,旅游地内的细粒度地名贯穿了整个旅游过程,起到了景观符号和旅游记忆载体的作用。文章阐述了细粒度旅游地地名的概念和其研究意义,选取样本旅游地对网络上旅游文本中的细粒度地名进行统计分析,总结出细粒度旅游地地名的4个特点:尺度小、定位明确、形象直观和承载记忆;以Apache服务器和MySQL为平台,构建细粒度地名数据库;结合百度地图API和PHP语言编程,实现细粒度地名的识别与可视化方法。以黄山风景区为研究样例,构建了旅游地细粒度地名可视化应用实例,提供了一种地图辅助的旅游文本阅读模式。     

基于XML Schema的多源地名词典集成方法

目前,我国已经构建大量不同级别、面向不同应用的地名词典,网络大众化地名服务成为地名词典的必然发展趋势。该文提出一种基于XML Schema的多源异构地名词典集成方法,以XML Schema对地名词典进行数据描述,采用XSLT数据转换方法,运用MapForce软件,快速进行地名词典的数据结构映射,能够有效解决地名词典的跨平台及数据类型不统一问题。     

数字地名词典中的类型表达和管理

在数字地名词典中恰当地表达和管理地名的类型知识,有助于数字地名词典快速、有效地处理地名相关查询.为此,该文首先从数字地名词典的应用需求出发,分析类型在数字地名词典中的作用,进而设计一个地名类型本体模型.该模型表达了地名类型之间的继承关系以及对空间关系的约束.在本体模型基础上,提出了相应的查询处理策略并进行了系统实现.     

Reference data enhancement for geographic information retrieval using linked data

Gazetteers are instrumental in recognizing place names in documents such as Web pages, news, and social media messages. However, creating and maintaining gazetteers is still a complex task. Even though some online gazetteers provide rich sets of geographic names in planetary scale (e.g. GeoNames), other sources must be used to recognize references to urban locations, such as street names, neighborhood names or landmarks. We propose integrating Linked Data sources to create a gazetteer that combines a broad coverage of places with urban detail, including content on geographic and semantic relationships involving places, their multiple names and related non‐geographic entities. Our final goal is to expand the possibilities for recognizing, disambiguating and filtering references to places in texts for geographic information retrieval (GIR) and related applications. The resulting ontological gazetteer, named LoG (Linked OntoGazetteer), is accessible through Web services by applications and research initiatives on GIR, text processing, named entity recognition and others. The gazetteer currently contains over 13 million places, 140 million attributes and relationships, and 4.5 million non‐geographic entities. Data sources include GeoNames, Freebase, DBPedia and LinkedGeoData, which is based on OpenStreetMap data. An analysis on how these datasets overlap and complement one another is also presented.     

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.     

A context-aware personalized travel recommendation system based on geotagged social media data mining

The proliferation of digital cameras and the growing practice of online photo sharing using social media sites such as Flickr have resulted in huge volumes of geotagged photos available on the Web. Based on users' traveling preferences elicited from their travel experiences exposed on social media sites by sharing geotagged photos, we propose a new method for recommending tourist locations that are relevant to users (i.e., personalization) in the given context (i.e., context awareness). We obtain user-specific travel preferences from his/her travel history in one city and use these to recommend tourist locations in another city. Our technique is illustrated on a sample of publicly available Flickr dataset containing photos taken in various cities of China. Results show that our context-aware personalized method is able to predict tourists' preferences in a new or unknown city more precisely and generate better recommendations compared to other state-of-the-art landmark recommendation methods.     

郭子章地理学著述成就述论

明万历朝的军事家、政治家郭子章,战功卓著,政绩突出,也是一位成就出色的大学者,其地理学尤其是历史人文地理方面的著述极为丰富。渊博的知识、非凡的经历,加上严谨的治学精神,造就了其地理学著述的价值。其地理学著述中,《郡县释名》是我国历史上最早的一部专门注释地名渊源的著作,在地名学史上占有重要地位;《黔记》、《豫章书》等大量方志,涉及江西、贵州等省之自然、人文地理内容;此外,郭子章在西南民族地区行政区划方面的探索与实践也在《黔草》等地方文献中多有体现。这些地理学著述为研究郭子章地理学思想、历代地名沿革、明代西南地区自然和人文地理提供了宝贵的资料。     

Geo-referencing with semi-automatic gazetteer expansion using lexico-syntactical patterns and co-reference analysis

Geo-referencing is a key task for geographical information retrieval because it allows unstructured or textual documents (i.e., Web pages) to be associated with geographical locations, which are then used by geo-search engines to index documents and search information by spatial criteria. This work proposes a strategy to extract geo-references from textual documents that combine natural language-processing techniques and co-reference solving heuristics, which in turn can be used to expand a geographical gazetteer. Implicit geographical entities (i.e., those entities referred to by pronouns) are recognized and incorporated into the gazetteer that is updated and used for geo-referencing tasks. Experiments show the promise of the approach to geo-referencing Web pages when dealing with implicit and/or indirect geo-references.