document: LINDEN : linking named entities with knowledge base via semantic knowledge Integrating the extracted facts with an existing knowledge base has raised an urgent need to address the problem of entity linking . Specifically , entity linking is the task to link the entity mention in text with the corresponding real world entity in the existing knowledge base . However , this task is challenging due to name ambiguity , textual inconsistency , and lack of world knowledge in the knowledge base . Several methods have been proposed to tackle this problem , but they are largely based on the co-occurrence statistics of terms between the text around the entity mention and the document associated with the entity . In this paper , we propose LINDEN , a novel framework to link named entities in text with a knowledge base unifying Wikipedia and WordNet , by leveraging the rich semantic knowledge embedded in the Wikipedia and the taxonomy of the knowledge base . We extensively evaluate the performance of our proposed LINDEN over two public data sets and empirical results show that LINDEN significantly outperforms the state-of-the-art methods in terms of accuracy .
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