Entity markup for knowledge base population

Abstract

Entities (e.g. people, places, products) exist in various heterogeneous sources, such as Wikipedia, web page, and social media. Entity markup, like entity extraction, coreference resolution, and entity disambiguation, is the essential means for adding semantic value to unstructured web contents and this way enabling the linkage between unstructured and structured data and knowledge collections. A major challenge in this endeavor lies in the ambiguity of the digital contents, with context-dependent semantic and dynamic. In this paper, I introduce the main challenges of coreference resolution and named entity disambiguation. Especially, I propose practical strategies to improve entity markup. Furthermore, experimental studies are conducted to fulfill named entity disambiguation in combination with the optimized entity extraction and coreference resolution. The main goal of this paper is to analyze the significant challenges of entity markup and present insights on the proposed entity markup framework for knowledge base population. The preliminary experimental results prove the significance of improving entity markup.