Details
| Original language | English |
|---|---|
| Title of host publication | The Semantic Web – ISWC 2019 |
| Subtitle of host publication | 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings, Part I |
| Editors | Chiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtech Svátek, Isabel Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, Fabien Gandon |
| Pages | 200-218 |
| Number of pages | 19 |
| Edition | 1. |
| ISBN (electronic) | 9783030307936 |
| Publication status | Published - 17 Oct 2019 |
| Event | 18th International Semantic Web Conference, ISWC 2019 - Auckland, New Zealand Duration: 26 Oct 2019 → 30 Oct 2019 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11778 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: (1) prediction of sub-event relations, and (2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52% points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings, Part I. ed. / Chiara Ghidini; Olaf Hartig; Maria Maleshkova; Vojtech Svátek; Isabel Cruz; Aidan Hogan; Jie Song; Maxime Lefrançois; Fabien Gandon. 1. ed. 2019. p. 200-218 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11778 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - HapPenIng
T2 - 18th International Semantic Web Conference, ISWC 2019
AU - Gottschalk, Simon
AU - Demidova, Elena
N1 - Funding Information: This work was partially funded by the EU Horizon 2020 under MSCA-ITN-2018 “Cleopatra” (812997), and the Federal Ministry of Education and Research, Germany (BMBF) under “Simple-ML” (01IS18054).
PY - 2019/10/17
Y1 - 2019/10/17
N2 - Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: (1) prediction of sub-event relations, and (2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52% points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.
AB - Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: (1) prediction of sub-event relations, and (2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52% points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.
UR - http://www.scopus.com/inward/record.url?scp=85075736163&partnerID=8YFLogxK
U2 - 10.48550/arXiv.1909.06219
DO - 10.48550/arXiv.1909.06219
M3 - Conference contribution
AN - SCOPUS:85075736163
SN - 9783030307929
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 200
EP - 218
BT - The Semantic Web – ISWC 2019
A2 - Ghidini, Chiara
A2 - Hartig, Olaf
A2 - Maleshkova, Maria
A2 - Svátek, Vojtech
A2 - Cruz, Isabel
A2 - Hogan, Aidan
A2 - Song, Jie
A2 - Lefrançois, Maxime
A2 - Gandon, Fabien
Y2 - 26 October 2019 through 30 October 2019
ER -