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A multi-scale, multi-context ontology for fusion and fission

A multi-scale, multi-context ontology for fusion and fission. Data Fusion and Separation Meeting June 24-26, 2001, Carnuntum, Austria. Margarita Kokla & Marinos Kavouras National Technical University of Athens. Central notion: multi-scale, multi-context ontology.

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A multi-scale, multi-context ontology for fusion and fission

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  1. A multi-scale, multi-context ontologyfor fusion and fission Data Fusion and Separation Meeting June 24-26, 2001, Carnuntum, Austria • Margarita Kokla & Marinos Kavouras • National Technical University of Athens

  2. Central notion: multi-scale, multi-context ontology • Extend the notion of multi-scale data to include, except from different levels of detail, different conceptualizations of geographic entities. • fusion of heterogeneous ontologies • fission: production of ontologies for specific uses • generalization

  3. Integration and ontology research • Ontologies play important role in information integration. • A top-level ontology may provide the framework for integration (Guarino, 1998; Sowa 2000). • Fusion of different geographic domain ontologies (SDTS) with top-level ontologies (CYC, WordNet) for information exchange and reuse (Kokla & Kavouras, 2001) • Diversity of existent top-level ontologies (CYC, WordNet, Mikrokosmos) • Solution: embody theories of geographic information cognition and human categorization (Smith & Mark, 2000)

  4. Principles of categorization and dimensions of categorical systems (Rosch, 1976) Vertical level of abstraction Horizontal internal structure Principles of categorization: • cognitive economy • perceived world structure Dimensions of categorical systems: • horizontal dimension:internal structure of categories • vertical dimension: level of abstraction Categorical systems categories are conceived in terms of their clear cases rather than their boundaries basic level categorization = the most inclusive level with attributes common to all or most members of the category

  5. Empirical evidence for the basic level • 9 taxonomies, e.g., tree, bird, furniture - 3 levels of abstraction (attributes in common, motor movements in common, similarity in shape) (Rosch, 1976) • 3 taxonomies (artificial and natural categories) - 3 levels of abstaction

  6. Basic level categories • minimize ambiguity and maximize comprehension. • increase similarity, simplicity and commonality in user interaction (accessibility to a wider range of users). • help to resolve conflicts during the integration of complex categories.

  7. Integration process • analysis of entity types-classes, attributes: • identification of heterogeneities in definitions and relationships between classes (equivalence, overlap, etc.) • semantic factoring • correspondences between attributes • creation of the integrated ontology

  8. Two projects 1. Integration of: • CORINE Land Cover nomenclature for scales 1:100,000–1:1,000,000 • Cadastral classification of land use characteristics developed by the Hellenic Mapping & Cadastral Organization referring to scales 1:1,000–1:5,000 2. Definition of new land use/cover categories for conducting the 2001 agricultural census by Hellenic Statistical Service, associated with: • the former classification for conducting the 1991 agricultural census • CLUSTERS (Classification of Land Use Statistics Eurostat Remote Sensing Programme) • CORINE Land Cover nomenclature

  9. Semantic factoring commerce Industrial or commercial units Tertiary sector • decomposition of overlapping classes into fundamental, disjoint classes which: • constitute the most clear, unambiguous and coherent classes (elementary classes or building blocks of the categorization) • reflect the consensus across different conceptualizations of geographic entities • revelation of basic level categories during integration

  10. Semantic factoring • the levels above and beneath the basic level result from synthesis and analysis correspondingly. • subordinate level: specialization of basic level, includes expert knowledge • superordinate level: abstract, usually artificial classes, e.g., «forests and semi-natural areas» (CLC) heterogeneity may occur as a result of different conceptualizations of space, e.g., land cover perspective (artificial surfaces, agricultural areas, waterbodies) vs. economic perspective (primary, secondary, tertiary sector).

  11. Technical and transport infrastructures Industrial, commercial and transport units Transport CORINE Land Cover CLUSTERS Technical Infrastructures Industrial or commercial units Semantic Factoring (extraction of basic-level categories)

  12. Correspondence of attributes

  13. Cross-table of the integrated context (ascribe attributes to basic-level categories)

  14. Creation of the integrated categorization • INPUT: cross-table of the integrated context • OUTPUT: set of final concepts and order relationships • modeling of basic categories, attributes, concepts and relationships using Formal Concept Analysis

  15. Posets and trees • An ordered set (or partially ordered set) (P, ) is a set P with an order relation  defined on that set. • A binary relation on a set P is called an order relation () if for all elements, x, y, z  P the following conditions are satisfied: x  x (reflexivity) x  y and y  x implies that x = y (antisymmetry) x  y and y  z implies that x  z (transitivity) • In a poset an element may have multiple parents rather than being limited to one as in the case for trees. A poset is therefore a generalization of a tree.

  16. Lattices tree poset lattice a collection of sets such that for any two overlapping sets in the collection, the intersection of the sets is also in the collection Let P be a partially ordered set. Then: If for any two elements x, y  P the least upper bound x  y and greatest lower bound x  y always exist, then P is called a lattice. If the greatest lower bound  S and least upper bound  S exist for all S  P, then P is called a complete lattice.

  17. Concept Lattices Formal Concept Analysis(Wille, 1992) • Formal Context: a triple (G, M, I) where G and M aresets of objects and attributes and I is a binary relation between G and M. • Incidence relation gIm: the object g has the attribute m. • Definition: For a set A  G of objects and a set B  M of attributes we define: A' = {mM gIm for all gA} B' = {gG gIm for all mB} • Formal Concept, Conceptual Class or Category:collection of entities or objects exhibiting one or more common characteristics or attributes. A pair (A, B) is a formal concept of the context (G, M, I) if AG, BM, A=B and B=A, whereAis called theextentandBtheintent of the formal concept.

  18. Concept Lattices Formal Concept Analysis(Wille, 1992) • Superconcept/subconcept relation:the concept (A1, B1) is a subconcept of the concept (A2, B2) ( (A1, B1)  (A2, B2)), if A1 A2 (which is equivalent to B2 B1). (A2, B2) is then a superconcept of (A1, B1). • Concept Lattice: the set of all concepts of (G, M, I) ordered by the subconcept-superconcept relation is called the concept lattice of the context (G, M, I) and is denoted by B(G, M, I). • Basic Theorem on Concept Lattices: Let (G, M, I) be a context. Then B(G, M, I) is a complete lattice in which the greatest lower bound (meet) and the least upper bound (join) are given by:

  19. Creation of the integrated categorization • incorporate multiple relationships • creation of extra categories based on the fusion or division of original ones: the least upper bound (join) and the greatest lower bound (meet) are given by definition • allow overlap, overcome the rigidity of tree structures • matrices in case of many classes and relationships

  20. Algorithm for Creating Concept Lattices • step 1. Draw the list of object intents or attribute extents: {g}' = {m  M gIm} {m'} = {g  G gIm} • step 2. Use either the formulas: • substep 2.1. The intent M is entered into the list. • substep 2.m. For each set A’ entered into the list in an earlier step, we form the set: A'  g' and include it in the list, provided that it is not already contained within it.

  21. Formal Concepts of the integrated context

  22. Matrix Manipulations (for large contexts) Matrix M mij = 1 if concept Ci is subconcept of Cj mij = 0 otherwise Matrix L L = M-M*M lij = 1 if concept Ci is directly below Cj

  23. Excerpt of the Integrated Concept Lattice

  24. Excerpt of the Integrated Concept Lattice (project 1) CORINE Land Cover Hellenic Cadastre Common classes

  25. DIGEST- CADASTRE CORINE DIGEST ALL THREE CADASTRE Excerpt of the Integrated Concept Lattice (project 1) C1 ARTIFICIAL SURFACES C13 Tertiary sector C2 C5 Industrial, commercial Artificial, and transport units non-agricultural vegetated areas C7 C3 C11 C12 Trasportation Industrial or Green urban Sport and leisure commercial units areas facilities C10 C19 C20 C6 C4 Commerce Port areas Airports Secondary Road and rail sector networks C8 C9 C14 C15 C16 C17 C18 Manufcturing Energy Road network Railway Intersection Terminal Parking Associated Processing Fabrication Industrial Industry Industry Structures

  26. Excerpt of the Integrated Concept Lattice (project 2)

  27. Fission «vertical» and «horizontal» integration vertical: level of detail horizontal: context (conceptualization, domain, application, etc.) Level of detail Context Classes are defined only by level of detail and context. Other parameters (e.g., spatial characteristics) are not dealt with. (e.g., building and building block)

  28. Fission • Given a scale and a context, the CL makes it possible to determine the appropriate «band» and derive the classes to be used. • Different levels of detail correspond to «horizontal lines» (or «bands») in the CL. • Different contexts correspond to «vertical lines» in the CL.

  29. Schema fission: Different levels of detail

  30. Schema fission: Different Contexts a context

  31. Generalization • The structure of the CL enables links between similar classes at different levels of detail • Dynamic generalization of geographic entities: transfer from one level of detail to the other, continuous on-the-fly generalization on the screen depending on the zoom factor • Generalization through time: links correspond to the evolution of classes through time

  32. Dynamic model generalization process Transition to different levels of detail and different classification schemata by changing the level of detail and the context. 1:100.000 1:50.000 1:20.000 1:10.000 1:5000

  33. Conclusion • development of a multi-scale, multi-context ontology for: • fusion • fission • generalization • revelation of implicit relationships between concepts • derivation of new classes from the fusion or division of originally overlapping ones (increase semantic completeness) • preservation of original ontologies

  34. Conclusion • the CL incorporates different complementary conceptualizations, each suitable for some context and level of detail • fission: selection of appropriate categories according to the context and level of detail of specific applications facilitates information reuse • cognition should not be ignored in the integration of different ontologies - embody theories of human categorization

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