Semantic Enhancement Engine: A Modular Document Enhancement Platform for Semantic Applications over Heterogeneous Content

Traditionally, automatic classification and metadata extraction have been performed in isolation, usually on unformatted text. SCORE Enhancement Engine (SEE) is a component of a Semantic Web technology called the Semantic Content Organization and Retrieval Engine (SCORE). SEE takes the next natural steps by supporting heterogeneous content (not only unformatted text), as well as following up automatic classification with extraction of contextually relevant, domain-specific (i.e., semantic) metadata. Extraction of semantic metadata not only includes identification of relevant entities but also relationships within the context of relevant ontology. This paper describes SEE's architecture, which provides a common API for heterogeneous document processing, with discrete, reusable and highly configurable modular components. This results in exceptional flexibility, extensibility and performance. Referred to as SEE modules (SEEMs), which are divided along functional lines, these processors perform one of the following roles: restriction (determine the segments of the input text to operate upon); enhancement (discover textual features of semantic interest); filtering (augment, remove or supplement the features recognized); or outputting (generate reports, annotate the original, update databases, or other actions). Each SEEM manages its configuration options and is arranged serially in virtual pipelines to perform designated semantic tasks. These configurations can be saved and reloaded on a per-document basis. This allows a single SEE installation to act logically as any number of Semantic Applications, and to compose these Semantic Applications as needed to perform even more complex semantic tasks. SEE leverages SCORE's unique approach of creating and using large knowledge base in semantic processing. It enables SCORE to provide flexible handling of highly heterogeneous content (including raw text, HTML, XML and documents of various formats); reliable automatic classification of documents; accurate extraction of semantic, domain-specific metadata; and extensive management of the enhancement processes including various reporting and semantic annotation mechanisms. This results in SCORE's advanced capability in heterogeneous content integration at a higher semantic level, rather than syntactical and structural level approaches based on XML and RDF, by supporting and exploiting domain specific ontologies. This work also presents an approach to automatic semantic annotation, a key scalability challenge faced in realizing the Semantic Web.