Learning conflict resolution strategies for cross-language Wikipedia data fusion

Bryl, Volha; Bizer, Christian

doi:10.1145/2567948.2578999

Cited by 33 publications

(33 citation statements)

References 12 publications

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“…One of such solutions is DBpedia. The problem that is often encountered is a conflict resolution, which occurs when various language versions concerning the same subject have conflicting information [21]. Our quality metrics can help in building more effective conflict resolution strategies for data fusing.…”

Section: Discussionmentioning

confidence: 99%

Relative Quality and Popularity Evaluation of Multilingual Wikipedia Articles

Lewoniewski¹,

Węcel²,

Abramowicz³

2017

Preprint

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Despite the fact that Wikipedia is often criticized for its poor quality, it continues to be one of the most popular knowledge base in the world. Articles in this free encyclopedia on various topics can be created and edited in about 300 different language versions independently. Our research showed that in language sensitive topics quality of information can be relatively better in the relevant language versions. However, in most cases it is difficult for the Wikipedia readers to determine the language affiliation of the described subject. Additionally, each language edition of Wikipedia can have own rules in manual assessing of the content quality. This makes automatic quality comparison of articles between various languages a challenging task. The paper presents results of relative quality and popularity assessment of over 28 million articles in 44 selected language versions. In addition, a comparative analysis of the quality and popularity of articles in some topics was conducted. The proposed method allows to find articles with information of better quality that can be used to automatically enrich other language editions of Wikipedia.

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Section: Discussionmentioning

confidence: 99%

Relative Quality and Popularity Evaluation of Multilingual Wikipedia Articles

Lewoniewski¹,

Węcel²,

Abramowicz³

2017

Preprint

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“…Recently, much work in Natural Language Processing focused on Knowledge Base Completion (Nickel et al, 2016a, KBC), the task of enriching and refining existing KBs. Many different methods have been explored for KBC, including exploitation of resources such as text corpora (Snow et al, 2006;Mintz et al, 2009;Aprosio et al, 2013) or other KBs Bryl and Bizer, 2014) for acquiring additional knowledge. Alternative approaches, in contrast, primarily rely on existing information from the KB itself (Socher et al, 2013;Nickel et al, 2016b) used as ground-truth to simultaneously learn continuous representations of KB concepts and relations, which are used to infer additional KB relations.…”

Section: Related Workmentioning

confidence: 99%

The ContrastMedium Algorithm: Taxonomy Induction From Noisy Knowledge Graphs With Just A Few Links

Faralli

Panchenko

Biemann

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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In this paper, we present ContrastMedium, an algorithm that transforms noisy semantic networks into full-fledged, clean taxonomies. ContrastMedium is able to identify the embedded taxonomy structure from a noisy knowledge graph without explicit human supervision such as, for instance, a set of manually selected input root and leaf concepts. This is achieved by leveraging structural information from a companion reference taxonomy, to which the input knowledge graph is linked (either automatically or manually). When used in conjunction with methods for hypernym acquisition and knowledge base linking, our methodology provides a complete solution for end-to-end taxonomy induction. We conduct experiments using automatically acquired knowledge graphs, as well as a SemEval benchmark, and show that our method is able to achieve high performance on the task of taxonomy induction.

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“…External KBC approaches use outer knowledge like text corpora Aprosio et al, 2013) or other KBs Bryl and Bizer, 2014) for acquiring additional knowledge. The text-based external methods typically employ a form of a distant supervi-sion.…”

Section: Related Workmentioning

confidence: 99%

Improving Neural Knowledge Base Completion with Cross-Lingual Projections

Klein¹,

Ponzetto²,

Glavaš³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion. We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples. We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings). We then train the network with triples from the cross-lingually augmented knowledge base. Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.

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Learning conflict resolution strategies for cross-language Wikipedia data fusion

Cited by 33 publications

References 12 publications

Relative Quality and Popularity Evaluation of Multilingual Wikipedia Articles

Relative Quality and Popularity Evaluation of Multilingual Wikipedia Articles

The ContrastMedium Algorithm: Taxonomy Induction From Noisy Knowledge Graphs With Just A Few Links

Improving Neural Knowledge Base Completion with Cross-Lingual Projections

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