Description: Identifying topics and concepts associated with a set of documents is a task common to many applications. It can help in the annotation and categorization of documents and be used to to model a person's current interests for improving search results, business intelligence or selecting appropriate advertisements. One approach is to a associate a document with a set of topics selected from a fixed ontology or vocabulary of terms. We have investigated using Wikipedia's articles and associated pages as a topic ontology for this purpose. The benefits of this approach are that the ontology terms are developed through a social process, maintained and kept current by the Wikipedia community, represent a consensus view, and have meaning that can be understood simply by reading the associated Wikipedia page. We use Wikipedia articles and the category and article link graphs to to predict concepts common to a set of documents. We describe several algorithms that we implemented and evaluated to aggregate and refine results, including the use of spreading activation to select the most appropriate terms. While the Wikipedia category graph can be used to predict generalized concepts, the article links graph helps by predicting more specific concepts and concepts not in the category hierarchy. Our experiments show that it is possible to suggest new category concepts identified as a union of pages from the page link graph. Such predicted concepts can be used to define new categories or sub-categories within Wikipedia.

Type: Presentation

Authors: Tim Finin, Zareen Syed, and Anupam Joshi

Date: January 09, 2008

Tags: ontology, information retrieval, wikipedia

Format: Microsoft PowerPoint (Need a reader? Get one here)

Number of downloads: 125

Access Control: Publicly Available

Available for download as

size: 1718272 bytes