Abstract. We propose a mapping from the Enhanced Entity Relationship conceptual model to the W3C XML Schema Language with the following properties: information and integrity constraints are preserved, no redundance is introduced, different hierarchical views of the conceptual information are available, the resulting XML structure is highly connected, and the design is reversible. We investigate two different ways to nest the XML structure: a maximum connectivity nesting, that minimizes the number of schema constraints used in the mapping of the conceptual schema reducing the validation overhead, and a maximum depth nesting, that keeps low the number of (expensive) join operations that are necessary to reconstruct the information at query time using the mapped schema. We propose a graph-theoretic linear-time algorithm to find a maximum connectivity nesting and show that finding a maximum depth nesting is NP-complete. We complement our investigation with an implementation of the devised translation and we embed the implemented module in a software framework for the conceptual and logical design of spatio-temporal databases 1 .
Literature-based discovery tools have been often used to overcome the problem of fragmentation of science and to assist researchers in their process of cross-domain knowledge discovery.In this paper we propose a methodology for cross-domain literature-based discovery that focuses on outlier documents to reduce the search space of potential cross-domain links and to improve search efficiency. In a previous study, literature mining tools OntoGen for document clustering and CrossBee for cross-domain bridging term exploration were combined to search for hidden relations in scientific papers from two different domains of interest, where the utility of the approach was demonstrated in a study involving PubMed papers about Alzheimer's disease and gut microbiome. This paper extends the approach by proposing a methodology, implemented as a repeatable workflow in a web-based text mining platform TextFlows, which enables easy access and execution of the methodology for the interested researcher.
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