Details
| Original language | English |
|---|---|
| Pages (from-to) | 90-108 |
| Number of pages | 19 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 8787 |
| Publication status | Published - 2014 |
Abstract
Much of existing work in information extraction assumes the static nature of relationships in fixed knowledge bases. However, in collaborative environments such as Wikipedia, information and structures are highly dynamic over time. In this work, we introduce a new method to extract complex event structures from Wikipedia. We propose a new model to represent events by engaging multiple entities, generalizable to an arbitrary language. The evolution of an event is captured effectively based on analyzing the user edits history in Wikipedia. Our work provides a foundation for a novel class of evolution-aware entity-based enrichment algorithms, and considerably increases the quality of entity accessibility and temporal retrieval for Wikipedia. We formalize this problem and introduce an efficient end-to-end platform as a solution. We conduct comprehensive experiments on a real dataset of 1.8 million Wikipedia articles to show the effectiveness of our proposed solution. Our results demonstrate that we are able to achieve a precision of 70% when evaluated using manually annotated data. Finally, we make a comparative analysis of our work with the well established Current Event Portal of Wikipedia and find that our system WikipEvent using Co-References method can be used in a complementary way to deliver new and more information about events.
Keywords
- Clustering, Event detection, Temporal retrieval, Wikipedia
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8787, 2014, p. 90-108.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - WikipEvent
T2 - Leveraging wikipedia edit history for event detection
AU - Tran, Tuan
AU - Ceroni, Andrea
AU - Georgescu, Mihai
AU - Naini, Kaweh Djafari
AU - Fisichella, Marco
N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - Much of existing work in information extraction assumes the static nature of relationships in fixed knowledge bases. However, in collaborative environments such as Wikipedia, information and structures are highly dynamic over time. In this work, we introduce a new method to extract complex event structures from Wikipedia. We propose a new model to represent events by engaging multiple entities, generalizable to an arbitrary language. The evolution of an event is captured effectively based on analyzing the user edits history in Wikipedia. Our work provides a foundation for a novel class of evolution-aware entity-based enrichment algorithms, and considerably increases the quality of entity accessibility and temporal retrieval for Wikipedia. We formalize this problem and introduce an efficient end-to-end platform as a solution. We conduct comprehensive experiments on a real dataset of 1.8 million Wikipedia articles to show the effectiveness of our proposed solution. Our results demonstrate that we are able to achieve a precision of 70% when evaluated using manually annotated data. Finally, we make a comparative analysis of our work with the well established Current Event Portal of Wikipedia and find that our system WikipEvent using Co-References method can be used in a complementary way to deliver new and more information about events.
AB - Much of existing work in information extraction assumes the static nature of relationships in fixed knowledge bases. However, in collaborative environments such as Wikipedia, information and structures are highly dynamic over time. In this work, we introduce a new method to extract complex event structures from Wikipedia. We propose a new model to represent events by engaging multiple entities, generalizable to an arbitrary language. The evolution of an event is captured effectively based on analyzing the user edits history in Wikipedia. Our work provides a foundation for a novel class of evolution-aware entity-based enrichment algorithms, and considerably increases the quality of entity accessibility and temporal retrieval for Wikipedia. We formalize this problem and introduce an efficient end-to-end platform as a solution. We conduct comprehensive experiments on a real dataset of 1.8 million Wikipedia articles to show the effectiveness of our proposed solution. Our results demonstrate that we are able to achieve a precision of 70% when evaluated using manually annotated data. Finally, we make a comparative analysis of our work with the well established Current Event Portal of Wikipedia and find that our system WikipEvent using Co-References method can be used in a complementary way to deliver new and more information about events.
KW - Clustering
KW - Event detection
KW - Temporal retrieval
KW - Wikipedia
UR - http://www.scopus.com/inward/record.url?scp=84921690881&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-11746-1_7
DO - 10.1007/978-3-319-11746-1_7
M3 - Article
AN - SCOPUS:84921690881
VL - 8787
SP - 90
EP - 108
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SN - 0302-9743
ER -