Prototype to Investigate the Extent to Which Words with Specific Attributes Can Be Retrieved Using Granular Metadata

Abstract

Despite the growth in digital text collections, the ability to retrieve words or phrases with specific attributes is limited, for example, to retrieve words with a specific meaning within a specific section of a text. Many systems work with coarse bibliographic metadata. To enable fine-grained retrieval, it is necessary to encode texts with granular metadata. Sample texts were encoded with granular metadata. Five categories of metadata that can be used to capture additional data about texts were used, namely, morphological, syntactic, semantic, functional and bibliographic. A prototype was developed to parse the encoded texts and store the information in a database. The prototype was used to test the extent to which words or phrases with specific attributes could be retrieved. Retrieval on a detailed level was possible through the prototype. Retrieval using all five categories of metadata was demonstrated, as well as advanced searches using metadata from different categories in a single search. This article demonstrates that when granular metadata is used to encode texts, retrieval is improved. Relevant information can be selected, and irrelevant information can be excluded, even within a text.