Event Recognition on News Stories and Semi-Automatic Population of an Ontology
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Text Mining: Predictive Methods for Analyzing Unstructured Information
Text Mining: Predictive Methods for Analyzing Unstructured Information
Hermes: a semantic web-based news decision support system
Proceedings of the 2008 ACM symposium on Applied computing
The Unreasonable Effectiveness of Data
IEEE Intelligent Systems
Analyzing entities and topics in news articles using statistical topic models
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Construction and maintenance of a fuzzy temporal ontology from news stories
International Journal of Metadata, Semantics and Ontologies
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The identification of "actionable" information in news stories has become a popular area for investigation. News presents some unique challenges for the researcher. The size constraints of a news story often require that full background information is omitted. Although this is acceptable for a human reader, it makes any form of automatic analysis difficult. Computational analysis may require some background information to provide context to news stories. There have been some attempts to identify and store background information. These approaches have tended to use an ontology to represent relationships and concepts present in the background information. The current methods of creating and populating ontologies with background information for news analysis were unsuitable for our future needs. In this paper we present an automatic construction and population method of a domain ontology. This method produces an ontology which has the coverage of a manually created ontology and the ease of construction of the semi-automatic method. The proposed method uses a recursive algorithm which identifies relevant news stories from a corpus. For each story the algorithm tries to locate further related stories and background information. The proposed method also describes a pruning procedure which removes extraneous information from the ontology. Finally, the proposed method describes a procedure for adapting the ontology over time in response to changes in the monitored domain.