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A Comprehensive Extraction of Relevant Real-World-Event Qualifiers for Semantic Search Engines

Authors: Guillaume Bernard; Cyrille Suire; Cyril Faucher; Antoine Doucet;

A Comprehensive Extraction of Relevant Real-World-Event Qualifiers for Semantic Search Engines

Abstract

International audience; In this paper, we present an efficient and accurate method to represent events from numerous public sources, such as Wikidata or more specific knowledge bases. We focus on events happening in the real world, such as festivals or assassinations. Our method merges knowledge from Wikidata and Wikipedia article summaries to gather entities involved in events, dates, types and labels. This event characterization procedure is extended by including vernacular languages. Our method is evaluated by a comparative experiment on two datasets that shows that events are represented more accurately and exhaustively with vernacular languages. This can help to extend the research that mainly exploits hub languages, or biggest language editions of Wikipedia. This method and the tool we release will for instance enhance event-centered semantic search engines, a context in which we already use it. An additional contribution of this paper is the public release of the source code of the tool, as well as the corresponding datasets.

Country
France
Subjects by Vocabulary

Microsoft Academic Graph classification: Focus (computing) Source code Information retrieval Exploit business.industry Computer science Event (computing) media_common.quotation_subject Semantic search Vernacular Context (language use) Specific knowledge business media_common

Keywords

Event, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Information Retrieval, [INFO]Computer Science [cs], Linked and Open Data

32 references, page 1 of 4

1. Brank, J., Leban, G., Grobelnik, M.: Semantic Annotation of Documents. Informatica 42, 23{32 (Jan 2017)

2. Cybulska, A.K., Vossen, P.: Historical Event Extraction from Text. In: Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. pp. 39{43. Portland, Oregon, USA (Jun 2011), https://www.aclweb.org/anthology/W11-1506

3. Doddington, G., Mitchell, A., Przybocki, M., Ramshaw, L., Strassel, S., Weischedel, R.: The Automatic Content Extraction (ACE) program. Tasks, Data and Evaluation. In: Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC'04). pp. 837{840. Lisbon, Portugal (May 2004), http://www.lrec-conf.org/proceedings/lrec2004/pdf/5.pdf

4. Eberhard, David M., Gary F. Simons, Charles D. Fennig: Ethnologue: Languages of the World (2021), https://www.ethnologue.com/

5. Exner, P., Nugues, P.: Using semantic role labeling to extract events from wikipedia. In: DeRiVE@ ISWC. pp. 38{47 (2011)

6. Farber, M., Bartscherer, F., Menne, C., Rettinger, A.: Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Semantic Web 9(1), 77{129 (Nov 2017). https://doi.org/10.3233/SW-170275

7. Foundation, T.W.: List of Wikipedias. Wikipedia (Apr 2021), https://en. wikipedia.org/w/index.php?title=List_of_Wikipedias&oldid=1016309550

8. Foundation, T.W.: Wikipedia article depth - Meta (Apr 2021), https://meta. wikimedia.org/wiki/Wikipedia_article_depth

9. Foundation, T.W.: Wikipedia:Summary style. Wikipedia (Apr 2021), https://en.wikipedia.org/w/index.php?title=Wikipedia:Summary_style& oldid=1015628666

10. Gottschalk, S., Demidova, E.: EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation. Semantic Web 10(6), 1039{1070 (Oct 2019). https://doi.org/10.3233/SW-190355

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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    2
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
Funded by
EC| NewsEye
Project
NewsEye
NewsEye: A Digital Investigator for Historical Newspapers
  • Funder: European Commission (EC)
  • Project Code: 770299
  • Funding stream: H2020 | RIA
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