Retrieval, crawling and fusion of entity-centric data on the web

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dc.identifier.uri http://dx.doi.org/10.15488/1258
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1283
dc.contributor.author Dietze, Stefan
dc.date.accessioned 2017-04-05T12:01:23Z
dc.date.available 2017-04-05T12:01:23Z
dc.date.issued 2017
dc.identifier.citation Dietze, S.: Retrieval, crawling and fusion of entity-centric data on the web. In: Lecture Notes in Computer Science 10151 (2017), S. 3-16. DOI: https://doi.org/10.1007/978-3-319-53640-8_1
dc.description.abstract While the Web of (entity-centric) data has seen tremendous growth over the past years, take-up and re-use is still limited. Data vary heavily with respect to their scale, quality, coverage or dynamics, what poses challenges for tasks such as entity retrieval or search. This chapter provides an overview of approaches to deal with the increasing heterogeneity of Web data. On the one hand, recommendation, linking, profiling and retrieval can provide efficient means to enable discovery and search of entity-centric data, specifically when dealing with traditional knowledge graphs and linked data. On the other hand, embedded markup such as Microdata and RDFa has emerged a novel, Web-scale source of entitycentric knowledge. While markup has seen increasing adoption over the last few years, driven by initiatives such as schema.org, it constitutes an increasingly important source of entity-centric data on the Web, being in the same order of magnitude as the Web itself with regards to dynamics and scale. To this end, markup data lends itself as a data source for aiding tasks such as knowledge base augmentation, where data fusion techniques are required to address the inherent characteristics of markup data, such as its redundancy, heterogeneity and lack of links. Future directions are concerned with the exploitation of the complementary nature of markup data and traditional knowledge graphs. The final publication is available at Springer via http://dx.doi.org/ 10.1007/978-3-319-53640-8_1. eng
dc.language.iso eng
dc.publisher Heidelberg : Springer Verlag
dc.relation.ispartofseries Lecture Notes in Computer Science 10151 (2017)
dc.rights Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
dc.subject Dataset recommendation eng
dc.subject Entity retrieval eng
dc.subject Knowledge graphs eng
dc.subject Markup eng
dc.subject Schema.org eng
dc.subject Arches eng
dc.subject Data fusion eng
dc.subject Knowledge based systems eng
dc.subject Semantics eng
dc.subject Web crawler eng
dc.subject Entity retrieval eng
dc.subject Knowledge graphs eng
dc.subject Markup eng
dc.subject Schema.org eng
dc.subject Semantic Web eng
dc.subject.ddc 004 | Informatik ger
dc.title Retrieval, crawling and fusion of entity-centric data on the web
dc.type Text
dc.type article
dc.type conferenceObject
dc.relation.issn 0302-9743
dc.relation.doi https://doi.org/10.1007/978-3-319-53640-8_1
dc.bibliographicCitation.volume 10151
dc.bibliographicCitation.firstPage 3
dc.bibliographicCitation.lastPage 16
dc.description.version acceptedVersion
tib.accessRights frei zug�nglich


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