Access to Italian legal literature: Integration between Structured Repositories and Web Documents

The problems of accessing legal information and, in particular, legal literature are examined in conjunction with the creation of a portal to Italian legal doctrine. The design and implementation of services such as integrated access to a wide range of resources are described, with a focus on the importance of exploiting metadata assigned to disparate legal material. On the basis of the results of a survey of legal users’ requirements, the main features of the planned system are presented: accurate selection of resources, user profiling and assistance in the search process, reliance on rich and consistent metadata, navigation facilities for legal literature, legislation and case-law sources. The strategies devised have been experimented, such as the mapping of both the UNIMARC format and a proprietary citation format for journal articles to the Dublin Core unqualified metadata set and to DCMI cite. These formats are extracted respectively from OPACs and from a well-established legal bibliographic database, DoGi. Specific semantic problems of legal literature indexing are tackled, mainly concerning classification systems. Similar issues are present in legal doctrine available on the web. In particular web documents usually lack metadata and, where present, they do not usually conform to well-established metadata patterns. A research study was therefore carried out, followed by the creation of a prototype and testing with the aim of automatically providing web documents with DC metadata, thereby supporting the intellectual activity of a service provider in organizing qualified access services to web documents. The integration of structured repositories and web documents is the main purpose of the portal: it is constructed on the basis of a federation system with service provider functions, aiming at creating a centralized index of such resources. The index is based on a uniform metadata view created for structured data by means of the OAI approach and for web documents by a machine learning approach.