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Chapter VI. Accessibility: Critical GIS,... > Digital Libraries: Solutions and Pos...

Digital Libraries: Solutions and Possibilities

Dealing flexibly with differences among systems, ontologies, and data formats while respecting information sources' autonomy requires considerable thought. Adapting object-oriented digital libraries involves a number of solutions, such as mediation middleware, use and integration of Internet harvesting techniques, and new architectures based on object-oriented ontologies that affect the development of search modules and metadata description. Much of the research focuses on text-deciphering or linguistic equivalency algorithms or consensual activities on meanings of categories for resource description (Di Pasquale, Forlizzi, Jensen, Manolopoulos, Nardelli, Pfoser et al., 2003), interoperability of simple schemas with complex schemas, or designing frameworks for managing equivalencies between metadata models in different fields and languages (Baker & Klavans, 1998). Questions, such as best problem-solving practices or balance between human and machine for resource discovery, emerge. Since the transparent and integrated access to distributed and heterogeneous data sources is key to leveraging research, how would it be best to integrate data suitable for knowledge or information domains? Controlled annotation of semantic rich metadata with diverse types of data allows the use of sophisticated query schemes (Gertz & Sattler, 2003).

Information systems can address these questions by applying and extending metadata harvesting, and by building upon existing componentized frameworks (Ravindranathan, Shen, Gon¸alves, Fan, Fox, & Flanagan, 2004). Two very important issues are (1) how to reconcile the diversity found within the harvested data to create a single, integrated collection view for the end-user and (2) how to create an integrated framework that addresses data quality, flexible and efficient search, and scalability (Gon¸alves, France, & Fox, 2001). To provide technologies that improve the access to heterogeneous and distributed resources, several layers of metadata, related to users, communities, devices, and data sources, must be handled and efficiently used (Godard, Andrès, Grosky, & Ono, 2004). Further, in order to understand class and property hierarchies, support for inference should be available (Palmér, Naeve, & Paulsson, 2004). The quality of the information available to the information specialist to adequately address (matchmake) the semantics of requirements and resources is critical. The explicitness, structuring, and formality of this information can differ considerably leading to different types of matchmaking (Lutz et al., 2003).


  

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