Internationalization of Linked Data: The case of the Greek DBpedia edition

Kontokostas, Dimitris; Bratsas, Charalampos; Auer, Sören; Hellmann, Sebastian; Antoniou, Ioannis; Metakides, George

doi:10.1016/j.websem.2012.01.001

Cited by 34 publications

(11 citation statements)

References 9 publications

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“…Thanks to this first step, we increased the DBpedia coverage on Wikipedia articles by around 60% on the six languages considered in our experiments (English, Italian, German, French, Spanish, and Portuguese). The relative error of cross-lingual links in Wikipedia is very small, so we asses that the precision of the first phase is almost 100% [11].…”

mentioning

confidence: 82%

Automatic Expansion of DBpedia Exploiting Wikipedia Cross-Language Information

Aprosio

Giuliano

Lavelli

2013

Lecture Notes in Computer Science

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Abstract. DBpedia is a project aiming to represent Wikipedia content in RDF triples. It plays a central role in the Semantic Web, due to the large and growing number of resources linked to it. Nowadays, only 1.7M Wikipedia pages are deeply classified in the DBpedia ontology, although the English Wikipedia contains almost 4M pages, showing a clear problem of coverage. In other languages (like French and Spanish) this coverage is even lower. The objective of this paper is to define a methodology to increase the coverage of DBpedia in different languages. The major problems that we have to solve concern the high number of classes involved in the DBpedia ontology and the lack of coverage for some classes in certain languages. In order to deal with these problems, we first extend the population of the classes for the different languages by connecting the corresponding Wikipedia pages through cross-language links. Then, we train a supervised classifier using this extended set as training data. We evaluated our system using a manually annotated test set, demonstrating that our approach can add more than 1M new entities to DBpedia with high precision (90%) and recall (50%). The resulting resource is available through a SPARQL endpoint and a downloadable package.

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confidence: 82%

Automatic Expansion of DBpedia Exploiting Wikipedia Cross-Language Information

Aprosio

Giuliano

Lavelli

2013

Lecture Notes in Computer Science

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“…The DBpedia project [9,13,14] builds a large-scale, multilingual knowledge base by extracting structured data from Wikipedia editions in 111 languages. Wikipedia editions are extracted by the open source "DBpedia extraction framework" (cf.…”

Section: Fig 1 Overview Of Dbpedia Extraction Frameworkmentioning

confidence: 99%

“…More detailed statistics on the three corpora are available at the Web Data Commons page 9 . By publishing the data extracted from RDFa, Microdata and Microformats annotations, we hope on the one hand to initialize further domain-specific studies by third parties.…”

Section: Rdfa Microdata and Microformats Extraction Frameworkmentioning

confidence: 99%

Knowledge Base Creation, Enrichment and Repair

Hellmann

Bryl

Bühmann

et al. 2014

Lecture Notes in Computer Science

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Abstract. This chapter focuses on data transformation to RDF and Linked Data and furthermore on the improvement of existing or extracted data especially with respect to schema enrichment and ontology repair. Tasks concerning the triplification of data are mainly grounded on existing and well-proven techniques and were refined during the lifetime of the LOD2 project and integrated into the LOD2 Stack. Triplification of legacy data, i.e. data not yet in RDF, represents the entry point for legacy systems to participate in the LOD cloud. While existing systems are often very useful and successful, there are notable differences between the ways knowledge bases and Wikis or databases are created and used. One of the key differences in content is in the importance and use of schematic information in knowledge bases. This information is usually absent in the source system and therefore also in many LOD knowledge bases. However, schema information is needed for consistency checking and finding modelling problems. We will present a combination of enrichment and repair steps to tackle this problem based on previous research in machine learning and knowledge representation. Overall, the Chapter describes how to enable tool-supported creation and publishing of RDF as Linked Data (Sect. 1) and how to increase the quality and value of such large knowledge bases when published on the Web (Sect. 2).

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“…Following this argumentation, a community-built resource can only be efficiently extracted by a community-configured extractor. This argument is supported by the successful crowd-sourcing of DBpedia's internationalization [7] and the non-existence of open alternatives with equal extensiveness. Given these findings, we can now conclude four high-level requirements:…”

Section: Design and Implementationmentioning

confidence: 99%

Leveraging the Crowdsourcing of Lexical Resources for Bootstrapping a Linguistic Data Cloud

Hellmann

Brekle

Auer

2013

Semantic Technology

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Abstract. We present a declarative approach implemented in a comprehensive open-source framework based on DBpedia to extract lexicalsemantic resources -an ontology about language use -from Wiktionary. The data currently includes language, part of speech, senses, definitions, synonyms, translations and taxonomies (hyponyms, hyperonyms, synonyms, antonyms) for each lexical word. Main focus is on flexibility to the loose schema and configurability towards differing language-editions of Wiktionary. This is achieved by a declarative mediator/wrapper approach. The goal is to allow the addition of languages just by configuration without the need of programming, thus enabling the swift and resource-conserving adaption of wrappers by domain experts. The extracted data is as fine granular as the source data in Wiktionary and additionally follows the lemon model. It enables use cases like disambiguation or machine translation. By offering a linked data service, we hope to extend DBpedia's central role in the LOD infrastructure to the world of Open Linguistics.

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Internationalization of Linked Data: The case of the Greek DBpedia edition

Cited by 34 publications

References 9 publications

Automatic Expansion of DBpedia Exploiting Wikipedia Cross-Language Information

Automatic Expansion of DBpedia Exploiting Wikipedia Cross-Language Information

Knowledge Base Creation, Enrichment and Repair

Leveraging the Crowdsourcing of Lexical Resources for Bootstrapping a Linguistic Data Cloud

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