Using co‐occurrence models for placename disambiguation

Overell, Simon; Rüger, Stefan

doi:10.1080/13658810701626236

Cited by 69 publications

(60 citation statements)

References 19 publications

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“…A key problem is the ambiguity of toponyms [4,17]. The same place name can refer to different geographic entities (e.g., the Getty Thesaurus of Geographic Names returns 102 places for the toponym Santa Barbara; many of them being populated places).…”

Section: Gir'14mentioning

confidence: 99%

“…The rationale behind this approach is based on the cooccurrence model from existing works [17,12]. One geographic place is always associated with a unique set of other entities, such as nearby cities, higher-level administrative units, physical geographic features (e.g., rivers and moun- tains), and related persons.…”

Section: Integrating Dbpedia Entities For Disambiguationmentioning

confidence: 99%

“…The task of spotting is to extract the terms which can be used to represent entities in the world [17,13]. These terms are called surface forms, and this stage only identifies these surface forms, and does not try to discover which entity a surface form actually refers to.…”

Section: Stage 1: Spottingmentioning

confidence: 99%

“…Wikipedia is a knowledge base that has often been used as the source for ground truth descriptions [17,3,6,14]. Due to its plain text nature, Wikipedia articles have often been reduced to vector space models, in which the importance of a term is based on its frequency and inverse document frequency (TF-IDF).…”

Section: Gir'14mentioning

confidence: 99%

“…Context similarity is another metric used for named entity disambiguation. In existing works, such a metric has been performed using cooccurence models [17] as well as conceptual density [4]. In Wikipedia-based disambiguations, context similarity is often performed through vector space model which employs TF-IDF to assign weights to the vector terms [3,6].…”

Section: Context Similaritymentioning

confidence: 99%

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

Janowicz

Prasad

2014

Proceedings of the 8th Workshop on Geographic Information Retrieval

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Place name disambiguation is an important task for improving the accuracy of geographic information retrieval. This task becomes more challenging when the input texts are short. Wikipedia provides information about places and has often been employed for named entity recognition. However, the natural language representation of Wikipedia articles limits more effective use of this rich knowledge base. DBpedia is the Semantic Web version of Wikipedia, which provides structured and machine-understandable knowledge mined from Wikipedia articles. This paper presents an approach for combining Wikipedia and DBpedia to disambiguate place names in short texts. We discuss the pros and cons of the two knowledge bases, and argue that a combination of both performs better than each of them alone. We evaluate our proposed method by conducting experiments against baselines of three established methods. The result indicates that our method has a generally higher precision and recall. While our study employs DBpedia, the proposed method is generic and can be extended to other structured Linked Datasets such as Freebase or Wikidata.

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Section: Gir'14mentioning

confidence: 99%

Section: Integrating Dbpedia Entities For Disambiguationmentioning

confidence: 99%

Section: Stage 1: Spottingmentioning

confidence: 99%

Section: Gir'14mentioning

confidence: 99%

Section: Context Similaritymentioning

confidence: 99%

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

Janowicz

Prasad

2014

Proceedings of the 8th Workshop on Geographic Information Retrieval

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A random walk on an ontology: Using thesaurus structure for automatic subject indexing

Willis

Losee

2013

J Am Soc Inf Sci Tec

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Relationships between terms and features are an essential component of thesauri, ontologies, and a range of controlled vocabularies. In this article, we describe ways to identify important concepts in documents using the relationships in a thesaurus or other vocabulary structures. We introduce a methodology for the analysis and modeling of the indexing process based on a weighted random walk algorithm. . We also introduce a thesaurus-centric matching algorithm intended to improve the quality of candidate concepts. In all cases, the weighted random walk improves automatic indexing performance over matching alone with an increase in average precision (AP) of 9% for HEP, 11% for MeSH, 35% for NALT, and 37% for AGROVOC. The results of the analysis support our hypothesis that subject indexing is in part a browsing process, and that using the vocabulary and its structure in a thesaurus contributes to the indexing process. The amount that the vocabulary structure contributes was found to differ among the 4 thesauri, possibly due to the vocabulary used in the corresponding thesauri and the structural relationships between the terms. Each of the thesauri and the manual indexing associated with it is characterized using the methods developed here.

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Text Meets Space: Geographic Content Extraction, Resolution and Information Retrieval

Leidner

Martins

McDonough

et al. 2020

Lecture Notes in Computer Science

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In this half-day tutorial, we will review the basic concepts of, methods for, and applications of geographic information retrieval, also showing some possible applications in fields such as the digital humanities. The tutorial is organized in four parts. First we introduce some basic ideas about geography, and demonstrate why text is a powerful way of exploring relevant questions. We then introduce a basic end-toend pipeline discussing geographic information in documents, spatial and multi-dimensional indexing [19], and spatial retrieval and spatial filtering. After showing a range of possible applications, we conclude with suggestions for future work in the area.

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Using co‐occurrence models for placename disambiguation

Cited by 69 publications

References 19 publications

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

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

A random walk on an ontology: Using thesaurus structure for automatic subject indexing

Text Meets Space: Geographic Content Extraction, Resolution and Information Retrieval

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