COfEE: A comprehensive ontology for event extraction from text

Large volumes of data are constantly being published on the web; however, the majority of this data is often unstructured, making it difficult to comprehend and interpret. To extract meaningful and structured information from such data, researchers and practitioners have turned to Information Extraction (IE) methods. One of the most challenging IE tasks is Event Extraction (EE), which involves extracting information related to specific incidents and their associated actors from text. EE has broad applications, including building a knowledge base, information retrieval, summarization, and online monitoring systems. Over the past few decades, various event ontologies, such as ACE, CAMEO, and ICEWS, have been developed to define event forms, actors, and dimensions of events observed in text. However, these ontologies have some limitations, such as covering only a few topics like political events, having inflexible structures in defining argument roles, lacking analytical dimensions, and insufficient gold-standard data. To address these concerns, we propose a new event ontology, COfEE, which integrates expert domain knowledge, previous ontologies, and a data-driven approach for identifying events from text. COfEE comprises two hierarchy levels (event types and event sub-types) that include new categories related to environmental issues, cyberspace, criminal activity, and natural disasters that require real-time monitoring. In addition, dynamic roles are defined for each event sub-type to capture various dimensions of events. The proposed ontology is evaluated on Wikipedia events, and it is shown to be comprehensive and general. Furthermore, to facilitate the preparation of gold-standard data for event extraction, we present a language-independent online tool based on COfEE. A gold-standard dataset annotated by ten human experts consisting of 24,000 news articles in Persian according to the COfEE ontology is also prepared. To diversify the data, news articles from the Wikipedia event portal and the 100 most popular Persian news agencies between 2008 and 2021 were collected. Finally, we introduce a supervised method based on deep learning techniques to automatically extract relevant events and their corresponding actors.