Using the WordNet Ontology in the GeoCLEF Geographical Information Retrieval Task

Buscaldi, Davide; Rosso, Paolo; Arnal, Emilio Sanchis

doi:10.1007/11878773_105

Cited by 17 publications

(14 citation statements)

References 5 publications

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“…Due to the scope of this paper, our discussion will focus on methods using data from Wikipedia and DBpedia. Other data sources, such as WordNet, Getty Theauraus of Geographic Names (TGN), GEOnet Names Server (GNS), and Geographic Names Information System (GNIS) have also been used in existing works for place name disambiguation [19,18,5].…”

Section: Place Name Disambiguationmentioning

confidence: 99%

“…For example, when the term Washington appears in a sentence, Washington D.C. generally has a higher prior probability to be the referred entity than another populated place also called Washington. In some existing works, such entity prominence are incorporated into the disambiguation model through hand crafted rules (e.g., countries are more important than cities) [19,5].…”

Section: Entity Prominencementioning

confidence: 99%

“…While the count values provide useful estimates, it is often time consuming to crawl the Wikipedia knowledge base to derive them. DBpedia, on the other hand, presents the counts of related links for free through the properties of dbpedia-owl:wikiPageInLinkCount and dbpediaowl:wikiPageOutLinkCount (i.e., how many pages this article has linked out) 5 . These count values are derived when Wikipedia articles are converted into RDF, and therefore researchers can directly make use of these values instead of having to repeat the crawling process.…”

Section: Entity Prominencementioning

confidence: 99%

“…The second component is the indexing of data records in the target database. Better search results can be achieved, if the search engine can disambiguate the geographic entities mentioned in the natural language descriptions of data records, and establish an additional index based on the recognized entities [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Place Name Disambiguationmentioning

confidence: 99%

Section: Entity Prominencementioning

confidence: 99%

Section: Entity Prominencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…We investigated the use of automatic query expansion by means of WordNet [6] meronyms and synonyms in our previous participation to the GeoCLEF, but the proposed technique did not obtain good results [5,3]. Although there are some effective query expansion techniques [4] that can be applied to the geographical domain, we think that the expansion of the queries with synonyms and meronyms does not fit with the characteristics of the GeoCLEF task.…”

Section: Introductionmentioning

confidence: 99%

A WordNet-Based Indexing Technique for Geographical Information Retrieval

Buscaldi

Rosso

Sanchís

2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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Abstract. This paper presents an indexing technique based on WordNet synonyms and holonyms. This technique has been developed for the Geographical Information Retrieval task. It may help in finding implicit geographic information contained in texts, particularly if the indication of the containing geographical entity is omitted. Our experiments were carried out with the Lucene search engine over the GeoCLEF 2006 set of topics. Results show that expansion can improve recall in some cases, although a specific ranking function is needed in order to obtain better results in terms of precision.

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Spatial Querying Supported by Domain and User Ontologies: An Approach for Web GIS Applications

Derbal

Bordogna

Pasi³

et al. 2015

Advances in Intelligent Systems and Computing

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Using the WordNet Ontology in the GeoCLEF Geographical Information Retrieval Task

Cited by 17 publications

References 5 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 WordNet-Based Indexing Technique for Geographical Information Retrieval

Spatial Querying Supported by Domain and User Ontologies: An Approach for Web GIS Applications

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