For most, the web is the first source to answer a question formulated by curiosity, need, or research reasons. This phenomenon is due to the internet's ubiquitous access, ease of use, and the extensive and ever expanding content. The problem is no longer the need to acquire content to encourage use, but to provide organizational tools to support content categorization that will facilitate improved access methods. This paper presents the results of a new text characterization algorithm that combines semantic and linguistic techniques utilizing domain-based ontology background knowledge. It explores the combination of meronym, synonym, and hypernym linguistic relationships to create a set of concept chains used to represent concepts found in a document. The experiments show improved accuracy over bag-of-words based term weighting methods and reveal characteristics of the meronym contribution to document representation.