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During the past few years, the advent of distributed knowledge bases has shown the feasibility of realizing the promise of knowledge extraction. In particular, several platforms allow users to submit their own knowledge base documents or web pages. Some of these platforms allow user-generated content to be annotated by human experts (Hyvärinen, Mikkala, and Hall 2009; Mikkala and Hyvärinen 2010; Saeed et al. 2012; Yao et al. 2012). Merritt and Vranic (2003) proposed a knowledge graph framework to extend Google’s open directory project (Mei et al. 2003). The goal of this dissertation project was to contribute to this effort by designing techniques for identifying the core type of topic in a given document, and in particular, to create a ranking function to allow the annotators to report the core topic categories of documents. The purpose of this knowledge graph is to maintain and disseminate a global knowledge base of the Web (WebKnow) (Vranic and Merritt 2006). The framework consists of several main components. First, the Expr (expression) system allows users to define queries, rules, data values, and operators. Second, the Annot system is a web-based tool for submitting and publishing documents with human annotations, users can generate or reuse templates used by other annotators for annotating documents. Third, the Reader system reads the annotations in documents and creates a descriptive knowledge graph of the core topics discussed. In this work we used the Annot system to create templates that allowed us to annotate the core topics of web documents. Our results show that using the templates created by us allow annotators to identify the core topics in a simple and effective fashion. By using this framework, we were able to construct a system that allows annotators to answer the questions raised by Merritt and Vranic (2003). d2c66b5586