Context-Driven Semantic Enrichment of Italian News Archive

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6088)


Semantic enrichment of textual data is the operation of linking mentions with the entities they refer to, and the subsequent enrichment of such entities with the background knowledge about them available in one or more knowledge bases (or in the entire web). Information about the context in which a mention occurs, (e.g., information about the time, the topic, and the space, which the text is relative to) constitutes a critical resource for a correct semantic enrichment for two reasons. First, without context, mentions are “too little text” to unambiguously refer to a single entity. Second, knowledge about entities is also context dependent (e.g., speaking about political life of Illinois during 1996, Obama is a Senator, while since 2009, Obama is the US president). In this paper, we describe a concrete approach to context-driven semantic enrichment, built upon four core sub-tasks: detection of mentions in text (i.e., finding references to people, locations and organizations); determination of the context of discourses of the text, identification of the referred entities in the knowledge base, and enrichment of the entity with the knowledge relevant to the context. In such approach, context-driven semantic enrichment needs also to have contextualized background knowledge. To cope with this aspect, we propose a customization of Sesame, one of state-of-the-art knowledge repositories, to support representation and reasoning with contextualized knowledge. The approach has been fully implemented in a system, which has been practically deployed and applied to the textual archive of the local Italian newspaper “L’Adige”, covering the decade of years from 1999 to 2009.


Background Knowledge Entity Recognition Soccer Match Knowledge Repository Semantic Enrichment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.FBK, Center for Information Technology - IRSTPovo di TrentoItaly

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