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From unstructured to linked data: Entity extraction and disambiguation by collective similarity maximization
This video was recorded at 21st International Joint Conference on Artificial Intelligence (IJCAI), Pasadena. In this paper, we describe a pipeline of methods for identifying and resolving entities from unstructured data using a semi-structured background knowledge database. For this purpose, we employ named entity extraction, co-reference resolution and investigate performance of disambiguation using collective maximization of inter-entity similarity, compared to using only pair-wise disambiguation. We explore possibilities of using DBpedia and Yago as background knowledge databases with the goal of annotating unstructured text documents with global entity references.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
