Improving wikipedia-based place name disambiguation in short texts using structured data from DBpedia

Hu, Yingjie; Janowicz, Krzysztof; Prasad, Sathya

doi:10.1145/2675354.2675356

Cited by 28 publications

(24 citation statements)

References 17 publications

(18 reference statements)

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“…A more advanced usage of gazetteers is place name disambiguation (or toponym resolution). Since multiple place names can refer to the same place instance and the same place name can refer to different place instances, it is challenging to determine which place instance was referred to by a name in the text (Amitay et al 2004, Leidner 2008, Hu et al 2014. Gazetteers have been used in many ways for supporting place name disambiguation.…”

Section: Related Workmentioning

confidence: 99%

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

Mao

McKenzie

2018

International Journal of Geographical Information Science

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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 multi-scale 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.

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Section: Related Workmentioning

confidence: 99%

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

Mao

McKenzie

2018

International Journal of Geographical Information Science

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“…There are many open access KBs, such as Yago, DBpedia, and WordNet, that have been widely used for place name disambiguation [19], semantic search [20], knowledge discovery [21], and so on. A well-known application of KBs is Watson [22], a question-answering computer system that uses multiple information sources (including ontologies, encyclopedias, dictionaries, and other material) to build knowledge.…”

Section: Methodsmentioning

confidence: 99%

A Knowledge-Based Filtering Method for Open Relations among Geo-Entities

Qiu

Gao

et al. 2019

IJGI

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Knowledge graphs (KGs) are crucial resources for supporting geographical knowledge services. Given the vast geographical knowledge in web text, extraction of geo-entity relations from web text has become the core technology for construction of geographical KGs; furthermore, it directly affects the quality of geographical knowledge services. However, web text inevitably contains noise and geographical knowledge can be sparsely distributed, both of which greatly restrict the quality of geo-entity relationship extraction. We propose a method for filtering geo-entity relations based on existing knowledge bases (KBs). Accordingly, ontology knowledge, fact knowledge, and synonym knowledge are integrated to generate geo-related knowledge. Then, the extracted geo-entity relationships and the geo-related knowledge are transferred into vectors, and the maximum similarity between vectors is the confidence value of one extracted geo-entity relationship triple. Our method takes full advantage of existing KBs to assess the quality of geographical information in web text, which is helpful to improve the richness and freshness of geographical KGs. Compared with the Stanford OpenIE method, our method decreased the mean square error (MSE) from 0.62 to 0.06 in the confidence interval [0.7, 1], and improved the area under the receiver operating characteristic (ROC) curve (AUC) from 0.51 to 0.89.

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“…A unique feature of their work is the inclusion of textual distances between the mentioned places to quantify the weights of their co-occurrences. In some studies, the co-occurrences of places are combined with the cooccurrences of other types of entities, such as companies and universities, to improve the accuracy of place name disambiguation (Cucerzan 2007, Hu, Janowicz and Prasad 2014, Ju et al 2016). In addition, place name co-occurrences were also used for identifying the indeterminate boundary of vague place names (Jones et al 2008, Twaroch andJones 2010).…”

Section: Related Workmentioning

confidence: 99%

Extracting and analyzing semantic relatedness between cities using news articles

Shaw

2017

International Journal of Geographical Information Science

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News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topics as semantic relatedness. By reading news articles, one can grasp the general semantic relatedness between cities; yet, given hundreds of thousands of news articles, it is very difficult, if not impossible, for anyone to manually read them. This paper proposes a computational framework which can "read" a large number of news articles and extract the semantic relatedness between cities. This framework is based on a natural language processing model and employs a machine learning process to identify the main topics of news articles. We describe the overall structure of this framework and its individual modules, and then apply it to an experimental dataset with more than 500,000 news articles covering the top 100 U.S. cities spanning a 10-year period. We perform exploratory visualization of the extracted semantic relatedness under different topics and over multiple years. We also analyze the impact of geographic distance on semantic relatedness and find varied distance decay effects. The proposed framework can be used to support large-scale content analysis in city network research.

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Improving wikipedia-based place name disambiguation in short texts using structured data from DBpedia

Cited by 28 publications

References 17 publications

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

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

A Knowledge-Based Filtering Method for Open Relations among Geo-Entities

Extracting and analyzing semantic relatedness between cities using news articles

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