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Author, Editor

Author(s):

Suchanek, Fabian M.
Weikum, Gerhard

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Editor(s):





BibTeX cite key*:

Suchanek_2_2013

Title, Booktitle

Title*:

Knowledge harvesting in the big-data era.

Booktitle*:

Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2013

Event, URLs

URL of the conference:


URL for downloading the paper:


Event Address*:

New York, USA

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

22 June 2013

Event End Date:

27 June 2013

Publisher

Name*:

ACM

URL:


Address*:

New York, USA

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

933-938

Year*:

2013

VG Wort Pages:


ISBN/ISSN:


Sequence Number:


DOI:




Note, Abstract, ©


(LaTeX) Abstract:

The proliferation of knowledge-sharing communities such as Wikipedia and the progress in scalable information extraction from Web and text sources have enabled the automatic construction of very large knowledge bases. Endeavors of this kind include projects such as DBpedia, Freebase, KnowItAll, ReadTheWeb, and YAGO. These projects provide automatically constructed knowledge bases of facts about named entities, their semantic classes, and their mutual relationships. They contain millions of entities and hundreds of millions of facts about them. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, semantic search for entities and relations in Web and enterprise data, and entity-oriented analytics over unstructured contents. Prominent examples of how knowledge bases can be harnessed include the Google Knowledge Graph and the IBM Watson question answering system. This tutorial presents state-of-the-art methods, recent advances, research opportunities, and open challenges along this avenue of knowledge harvesting and its applications. Particular emphasis will be on the twofold role of knowledge bases for big-data analytics: using scalable distributed algorithms for harvesting knowledge from Web and text sources, and leveraging entity-centric knowledge for deeper interpretation of and better intelligence with Big Data.



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Access Level:

Internal

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Databases and Information Systems Group

Audience:

Expert

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{Suchanek_2_2013,
AUTHOR = {Suchanek, Fabian M. and Weikum, Gerhard},
TITLE = {Knowledge harvesting in the big-data era.},
BOOKTITLE = {Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2013},
PUBLISHER = {ACM},
YEAR = {2013},
PAGES = {933--938},
ADDRESS = {New York, USA},
}


Entry last modified by Andrea Ruffing, 01/30/2014
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Created
01/23/2014 03:03:18 PM
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Editor
Andrea Ruffing



Edit Date
23.01.2014 15:08:59