Event prediction based on evolutionary event ontology knowledge
| Autor: | Shu Guo, Lihong Wang, He Min, Mao Qianren, Jianxin Li, Li Xi, Dongxiao He, Hao Peng |
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| Rok vydání: | 2021 |
| Předmět: |
Decision support system
Focus (computing) Computer Networks and Communications business.industry Event (computing) Computer science 020206 networking & telecommunications 02 engineering and technology Ontology (information science) Machine learning computer.software_genre Pipeline (software) Domain (software engineering) Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Ontology Semantic analysis (knowledge representation) 020201 artificial intelligence & image processing Artificial intelligence business computer Software |
| Zdroj: | Future Generation Computer Systems. 115:76-89 |
| ISSN: | 0167-739X |
| Popis: | The evolution and development of breaking news events usually present regular patterns, leading to the happening of sequential events. Therefore, the analysis of such evolutionary patterns among events and prediction to breaking news events from free text is a valuable capability for decision support systems. Traditional systems tend to focus on contents distribution information but ignore the inherent regularity of evolutionary events. We introduce evolutionary event ontology knowledge (EEOK) structuring the evolutionary patterns in five different event domains, namely Explosion, Conflagration, Geological Hazard, Traffic Accident, Personal Injury. Based on EEOK which provides a representing general-purpose ontology knowledge, we also explore a framework with a pipeline semantic analysis procedure of event extraction, evolutionary event recognition, and event prediction. Since the evolutionary event under each event domain has different evolution patterns, our proposed event prediction model combines the event types to capture the inherent regulation of evolutionary events. Comparative analyses are presented to show the effectiveness of the proposed prediction model compared to other alternative methods. |
| Databáze: | OpenAIRE |
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