1. D2I Modena, 27 Aprile 2001 Methodologies and techniques for translating information from source to target data models Unità Responsabile: CS-RC Unità Coinvolte: CS-RC

2. Synthesis SDR-Network: a new conceptual model for representing information sources having different formats and structures The SDR-Network is a rooted labeled graph: Net(IS) = NS(IS) represents the set of nodes; each node is characterized by a name AS(IS) denotes a set of arcs; each arc can be represented by a triplet –S is the source node –T is the target node –L ST = [d ST, r ST ] is a label associated to the arc

3. Synthesis d ST is the semantic distance coefficient: –it indicates how much the concept expressed by T is semantically close to the concept expressed by S –this depends from the capability of the concept associated to T to characterize the concept associated to S r ST is the semantic relevance coefficient: it denotes the fraction of instances of the concept denoted by S whose complete definition requires at least one instance of the concept denoted by T

4. Synthesis A suitable metrics can be defined based on the SDR-Network for measuring the strength of the semantic relationships holding among concepts of the same information source The Path Semantic Distance PSD P of a path P in Net(D) is the sum of the semantic distance coefficients associated to the arcs constituting the path The Path Semantic Relevance PSR P of a path P in Net(D) is the product of the semantic relevance coefficients associated to the arcs constituting the path

5. Synthesis The CD-Shortest-Path (Conditional D-Shortest-Path) between two nodes N and N’ in Net(D) and including an arc A (denoted by  N, N’  A ) is the path having the minimum Path Semantic Distance among those connecting N and N’ and including A A D-Path n is a path P in Net(D) such that n  PSD P < n+1 The i-th neighborhood of an SDR-Network node x is:

6. Synthesis An SDR-Network relative to a University

7. Synthesis nbh(student,0) = {,,,,, } nbh(student,1) = {,,,, }

8. Synthesis Suitable algorithms can be derived which exploit the SDR-Network for extracting terminological and structural relationships among concepts belonging to different information sources Any source basically reduces to the representation of a set of concepts and a set of relationships among concepts Using nodes and arcs in an SDR-Network we are able to represent both these sets and, therefore, to model any given source In addition, semantic distance and relevance coefficients allow to describe also some implicit intra-source semantics and possibly to derive inter-source semantics as well.

9. Synthesis We have defined translation rules for obtaining an SDR-Network from: –an XML document –an OEM-Graph –an E/R scheme However, we argue that analogous translation rules can be defined from almost all conceptual models proposed in the literature for representing semi-structured information source to SDR-Network We have compared the features of the SDR-Network w.r.t. those relative to some other conceptual models proposed in the Literature

10. Open Problems and Future Work In the future we plan to: –complete the definition of techniques for both reconstructing the relative semantics and obtaining a global view of a group of information sources represented by the corresponding SDR-Networks –exploit the semantics derived with the support of the SDR- Network for: E-commerce Semantic Query Processing Data and Web Warehouses Advanced Web Search Engines