Nessun titolo diapositiva

1
Methodologies and techniques for translating
information from source to target data models
Unità Responsabile CS-RC
Unità Coinvolte CS-RC
D2I Modena, 27 Aprile 2001
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) lt NS(IS), AS(IS) gt
• 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 lt S, T, LST gt
• S is the source node
• T is the target node
• LST dST, rST is a label associated to the
  arc

3
Synthesis

• dST 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
• rST 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 PSDP 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 PSRP 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-Pathn is a path P in Net(D) such that n ?
  PSDP lt n1
• 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) lt student, id, 0,1 gt, lt
  student, name, 0,1 gt,
• lt student, birthdate, 0,1 gt, lt student,
  birthplace, 0,0.75 gt, lt student,
  enrollment_year, 0,0.75 gt, lt student, address,
  0.25,1 gt
• nbh(student,1) lt student, course, 1,1 gt, lt
  course, id, 0,1gt,
• lt course, name, 0,1 gt, lt course, year,
  0.25,0.66 gt,
• lt course, n_students, 0.25,0.33 gt, lt course,
  argument, 0.5,1 gt,
• lt student, exam, 1,0.75 gt, lt exam, id, 0,1
  gt,
• lt exam, date, 0.25,1 gt, lt exam, grade,
  0.25,1 gt,
• lt student, thesis, 1,0.25 gt, lt thesis, title,
  0.25,1 gt,
• lt thesis, subject, 0.25,1 gt

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