News Aggregating System Supporting Semantic Processing Based on Ontology
The significant increase in number of the online newspapers has been the cause of information overload for readers and organizations who occasionally deal with the news content management. There have been several systems designed to manually or automatically take information from multiple sources, reorganize and display them in a single place, which relatively makes it much more convenient for readers. However, the methods used in these systems for the aggregating and processing are still limited and insufficient to meet some public demands, especially those relate to the semantics of articles. This paper presents a technical solution for developing a news aggregating system where the aggregation is automatic and supports some semantic processing functions, such as categorizing, search for articles, etc. The proposed solution includes modeling the information structure of each online newspaper for aggregation and utilizing Ontology, along with keyphrase graphs, for building functions related to the semantics of articles. The solution is applied to build an experimental system dealing with Vietnamese online newspapers, with the semantic processing functions for articles in the field of Labor & Employment. This system has been tested and achieved impressive results.
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