Wikidata

Vrandečić, Denny; Krötzsch, Markus

doi:10.1145/2629489

Cited by 2,165 publications

(429 citation statements)

References 14 publications

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“…Table 10 shows anecdotal samples for illustration. [7], NELL [9], BabelNet [39] and Wikidata [54] extract SPO facts from Wikipedia and other sources. However, all of these are limited to a prespecified set of predicates: a few hundred in some, up to a few thousand in Freebase and Wikidata.…”

Section: Methods Under Comparisonmentioning

confidence: 99%

“…Knowledge bases (KBs) contain subject-predicate-object triples about entities and their properties. Popular KBs include DBpedia [2], Yago [51], Wikidata [54] and Freebase [7]. Their commercial counterparts at Google, Microsoft, Baidu, and others, provide Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

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Query-driven on-the-fly knowledge base construction

Nguyen¹,

Abujabal²,

Tran

et al. 2017

Proc. VLDB Endow.

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Today's openly available knowledge bases, such as DBpedia, Yago, Wikidata or Freebase, capture billions of facts about the world's entities. However, even the largest among these (i) are still limited in up-to-date coverage of what happens in the real world, and (ii) miss out on many relevant predicates that precisely capture the wide variety of relationships among entities. To overcome both of these limitations, we propose a novel approach to build on-the-fly knowledge bases in a query-driven manner. Our system, called QKBfly, supports analysts and journalists as well as question answering on emerging topics, by dynamically acquiring relevant facts as timely and comprehensively as possible. QKBfly is based on a semantic-graph representation of sentences, by which we perform three key IE tasks, namely named-entity disambiguation, co-reference resolution and relation extraction, in a light-weight and integrated manner. In contrast to Open IE, our output is canonicalized. In contrast to traditional IE, we capture more predicates, including ternary and higher-arity ones. Our experiments demonstrate that QKBfly can build high-quality, on-the-fly knowledge bases that can readily be deployed, e.g., for the task of ad-hoc question answering. PVLDB Reference Format:

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Section: Methods Under Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Query-driven on-the-fly knowledge base construction

Nguyen¹,

Abujabal²,

Tran

et al. 2017

Proc. VLDB Endow.

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show abstract

“…Knowledge graphs such as DBpedia [2], Yago [16] and WikiData [25] as well as similar industrial initiatives by Google, Bing, IBM, BBC, or Thomson Reuters have demonstrated that representing encyclopedic and factual knowledge using RDF and Linked Data is feasible.…”

Section: Related Workmentioning

confidence: 99%

Towards a Knowledge Graph for Science

Auer

Kovtun

Prinz

et al. 2018

Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

114

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The document-centric workflows in science have reached (or already exceeded) the limits of adequacy. This is emphasized by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. This presents an opportunity to rethink the dominant paradigm of document-centric scholarly information communication and transform it into knowledgebased information flows by representing and expressing information through semantically rich, interlinked knowledge graphs. At the core of knowledge-based information flows is the creation and evolution of information models that establish a common understanding of information communicated between stakeholders as well as the integration of these technologies into the infrastructure and processes of search and information exchange in the research library of the future. By integrating these models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work as information and research results can be seamlessly interlinked with each other and better matched to complex information needs. Furthermore, research results become directly comparable and easier to reuse. As our main contribution, we propose the vision of a knowledge graph for science, present a possible infrastructure for such a knowledge graph as well as our early attempts towards an implementation of the infrastructure. * This invited article accompanies Sören Auer's WIMS2018 keynote.

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“…Since Wikipedia infoboxes are partially populated through templates from Wikidata entries, extracting data from infoboxes instead of Wikidata itself adds an additional source of errors. Furthermore, unless all language versions of Wikipedia are used as a source, such an approach would even limit the amount of retrieved information due to Wikidata's inherent multi-lingual design as the knowledge base behind all Wikipedias (Vrandeĉić and Krötzsch 2014).…”

Section: Related Workmentioning

confidence: 99%

NECKAr: A Named Entity Classifier for Wikidata

Geiß

Spitz

Gertz

2018

Lecture Notes in Computer Science

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Abstract. Many Information Extraction tasks such as Named EntityRecognition or Event Detection require background repositories that provide a classification of entities into the basic, predominantly used classes location, person, and organization. Several available knowledge bases offer a very detailed and specific ontology of entities that can be used as a repository. However, due to the mechanisms behind their construction, they are relatively static and of limited use to IE approaches that require up-to-date information. In contrast, Wikidata is a community-edited knowledge base that is kept current by its userbase, but has a constantly evolving and less rigid ontology structure that does not correspond to these basic classes. In this paper we present the tool NECKAr, which assigns Wikidata entities to the three main classes of named entities, as well as the resulting Wikidata NE dataset that consists of over 8 million classified entities. Both are available at http:// event.ifi.uni-heidelberg.de/?page id=532.

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Wikidata

Abstract: This collaboratively edited knowledgebase provides a common source of data for Wikipedia, and everyone else.

Cited by 2,165 publications

References 14 publications

Query-driven on-the-fly knowledge base construction

Query-driven on-the-fly knowledge base construction

Towards a Knowledge Graph for Science

NECKAr: A Named Entity Classifier for Wikidata

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