Semantic Document Networks to Support Concept Retrieval

This chapter focuses on a framework to support advanced document storage and fast queries to retrieve documents based on concept-focused searches. These searches favour ‘semantic’ searches which evaluate and use the meanings of words and phrases, rather than ‘key-word’ searches. The framework rests on three stages: pre-processing (semantic analysis influences the storage quality within a semantic database), conceptualization (extraction of key concepts to establish document networks), and storage within a semantic database, facilitating advanced future retrieval. The objective is to decompose documents and extract all relevant information about structure and content to allow comprehensive storage in a semantic document network; including the interpretation according to domains, contexts, languages, or readers. For example, the word ‘trunk’ may refer to a storage area (in the context of motor vehicles), a clothes storage box (in the context of travelling), or an elephant’s appendage (in the context of a safari); see Figure 1. The arrows represent parameters associated with relations. There can be multiple meanings for the related words and it is only the clustering of words that provides the important context which provides readers with meaning; e.g., Safari is also the name of an Internet browser. A brief introduction to conceptualization and the semantic document network provides an overview of how information can be stored in an interlinked network. Using a short sample, we demonstrate the calculation of the semantic core using concept-based indexing and how the concepts are embedded within the existing semantic document network. Simon Boese University of Hamburg, Germany