TY - JOUR

T1 - Event Detection in Wikipedia Edit History Improved by Documents Web Based Automatic Assessment

AU - Fisichella, Marco

AU - Ceroni, Andrea

N1 - Funding Information: Acknowledgments: This work was partially funded by the European Commission in the context of the FP7 ICT projects ForgetIT (grant No. 600826), DURAARK (grant No. 600908), CUbRIK (grant No. 287704), and the ERC Advanced Grant ALEXANDRIA (grant No. 339233).

PY - 2021/8/4

Y1 - 2021/8/4

N2 - A majority of current work in events extraction assumes the static nature of relationships in constant expertise knowledge bases. However, in collaborative environments, such as Wikipedia, information and systems are extraordinarily dynamic over time. In this work, we introduce a new approach for extracting complex structures of events from Wikipedia. We advocate a new model to represent events by engaging more than one entities that are generalizable to an arbitrary language. The evolution of an event is captured successfully primarily based on analyzing the user edits records in Wikipedia. Our work presents a basis for a singular class of evolution-aware entity-primarily based enrichment algorithms and will extensively increase the quality of entity accessibility and temporal retrieval for Wikipedia. We formalize this problem case and conduct comprehensive experiments on a real dataset of 1.8 million Wikipedia articles in order to show the effectiveness of our proposed answer. Furthermore, we suggest a new event validation automatic method relying on a supervised model to predict the presence of events in a non-annotated corpus. As the extra document source for event validation, we chose the Web due to its ease of accessibility and wide event coverage. Our outcomes display that we are capable of acquiring 70% precision evaluated on a manually annotated corpus. Ultimately, we conduct a comparison of our strategy versus the Current Event Portal of Wikipedia and discover that our proposed WikipEvent along with the usage of Co-References technique may be utilized to provide new and more data on events.

AB - A majority of current work in events extraction assumes the static nature of relationships in constant expertise knowledge bases. However, in collaborative environments, such as Wikipedia, information and systems are extraordinarily dynamic over time. In this work, we introduce a new approach for extracting complex structures of events from Wikipedia. We advocate a new model to represent events by engaging more than one entities that are generalizable to an arbitrary language. The evolution of an event is captured successfully primarily based on analyzing the user edits records in Wikipedia. Our work presents a basis for a singular class of evolution-aware entity-primarily based enrichment algorithms and will extensively increase the quality of entity accessibility and temporal retrieval for Wikipedia. We formalize this problem case and conduct comprehensive experiments on a real dataset of 1.8 million Wikipedia articles in order to show the effectiveness of our proposed answer. Furthermore, we suggest a new event validation automatic method relying on a supervised model to predict the presence of events in a non-annotated corpus. As the extra document source for event validation, we chose the Web due to its ease of accessibility and wide event coverage. Our outcomes display that we are capable of acquiring 70% precision evaluated on a manually annotated corpus. Ultimately, we conduct a comparison of our strategy versus the Current Event Portal of Wikipedia and discover that our proposed WikipEvent along with the usage of Co-References technique may be utilized to provide new and more data on events.

KW - Clustering

KW - Event detection

KW - Event validation

KW - Temporal retrieval

KW - User edits

KW - Wikipedia

UR - http://www.scopus.com/inward/record.url?scp=85112184202&partnerID=8YFLogxK

U2 - 10.3390/bdcc5030034

DO - 10.3390/bdcc5030034

M3 - Article

AN - SCOPUS:85112184202

VL - 5

JO - Big Data and Cognitive Computing

JF - Big Data and Cognitive Computing

IS - 3

M1 - 34

ER -