1. Logics for Data and Knowledge Representation The DERA methodology for the development of domain ontologies Feroz Farazi Originally by Fausto Giunchiglia and Biswanath Dutta Modified by Feroz Farazi

2. Knowledge Representation (KR)  Abstraction of the world via models, of a particular domain or problem, which allow automatic reasoning and interpretation  Fundamental Goal  to represent knowledge in a manner that facilitates inferencing new knowledge (i.e. drawing conclusions) from the already known facts possibly encoded in a knowledge base 2

3.  According to (Crawford & Kuipers, 1990): A knowledge representation system must have  a reasonably compact syntax  a well defined semantics so that one can say precisely what is being represented  sufficient expressive power to represent human knowledge  an efficient, powerful and understandable reasoning mechanism  support in building large knowledge bases 3 Knowledge Representation Properties

4. Knowledge Representation Issues  KR issues:  How do people represent knowledge?  What is the nature of knowledge?  Do we have domain specific schema or generic, domain independent schema?  How much it needs to be expressive? 4

5. Ontology  “formal, explicit specification of a shared conceptualisation” [T. R. Gruber, 1993]  Models a domain consisting of a shared vocabulary with the definition of objects and/or concepts and their properties and relations  A structural framework for organizing information, and  used as a form of KR in the fields like, AI, SW, Lib. Sc., Inf. Architecture, etc.  Can be used also as a language resource 5

6. Ontology Properties  Some of the ontological properties are:  Extendable  Reusable  Flexible  Robust  … 6

7. Domain  An area of knowledge or field of study that we are interested in or that we are communicating about  Example:  Computer science, Artificial Intelligence, Soft computing, Social networks, …Library science, Mathematics, Physics, Chemistry, Agriculture, Geography, …  Music, Movie, Sculpture, Painting, …Food, Wine, Cheese, …Space,… 7

8. Domain  A domain can be decomposed into its several constituents, and  Each of them denotes a different aspect of entities  An example from Space domain: by region, by body of water, by landform, by populated places, by administrative division, by land, by agricultural land, by facility, by altitude, by climate,…  Each of these aspects is called facet 8

9. Facet  A hierarchy of homogeneous terms describing an aspect of the domain, where each term in the hierarchy denotes a different concept  E.g.,  Body of water(e.g., River, Lake, Pond, Canal), Landform (e.g., mountain, hill, ridge), facility (e.g., house, hut, farmhouse, hotel, resort), etc.  language facet (e.g., English, Hindi, Italian,), property facet, author facet, religion facet (e.g., Christian, Hindu, Muslim), commodity facet, etc.

10. DERA  a facet based knowledge organization framework  independent from any specific domain  allows building domain specific ontologies  mapping to Description Logic  logically sound  decidable  Developed by the UniTn KnowDive group 10

11. DERA Surface Structure  In the surface level, it has the following components:  D – Domain  E – Entity  R – Relation  A – Attribute 11 Domain (D)  A DERA domain is a tuple of, D =

12. Entity (E)  an elementary component that consists of entity classes and their instances, having either perceptual correlates or only conceptual existence in a domain in context. It can be represented as a pair E =  Where,  C = a set of entity classes or concepts representing the entities  E' = a set of entities (also called objects, instances or individuals), possibly, real world named entities, those are the instantiations of C 12

13. Entity (E)  Entity classes (C) :  Represent the essence of the domain under consideration;  Consist of the core classes representing a domain in context  E.g., Consider the following classes in context of Space domain:  Mountain, Hill, Lake, River, Canal, Province, City, Hotel,... 13

14. Entity (E)  Entity (E') :  the real world named entities  representations of the real world entities  E.g.,  The Himalaya, Monte Bondone, Lake Garda, Trento, Povo, Hotel America,... 14

15. Entity (E) 15 An example from the Space domain

16. Relation (R)  An elementary component consists of classes representing relations between entities R =  {r} is a set of relations  A relation r is a link between two entities (E')  Builds a semantic relation between the entities  E.g.,  Some relations (spatial) from Space domain: near, adjacent, inside, before, center, sideways, etc. 16

17. Attribute (A)  An elementary component consists of classes expressing the characteristics of entities A =  Where A' is a set of datatype attributes and C is a set of descriptive attributes  An attribute is any property, qualitative, quantitative or descriptive measure of an entity 17

18. Attribute (A) (contd…)  Datatype Attributes (A'):  The datatype attributes include the attribute classes that account the quality or quantity of an entity within a domain  E.g.,  latitude, longitude (of a place):  45 0 N, 18 0 S  altitude (of a mountain):  8000ft, 2400m.  high, low  depth (of a lake):  deep, shallow  100ft., 20m. 18

19. Attribute (A) (contd…)  Descriptive Attributes ( C ):  include the attribute classes that describe the entities under a domain in consideration  value could consist of a single string (single valued) or a set of strings (multivalued)  E.g.,  natural resource (of a place):  coal, natural gas, oil, …  architectural style (of a castle):  {Classical architecture, Greek architecture, Roman architecture, Bauhaus, etc.}  history (of a place)  ……….  climbing route (to a mountain)  ………………. 19

20. Mapping  From DERA to DL  Entity classes (C) -> Concepts  Relations (R) -> Roles  Datatype attributes (A') -> Roles  Descriptive attributes ( C ) -> Roles  Entity (E') -> Individuals 20

21. Methodology  Step 1: Identification of the atomic concepts  Step 2: Analysis (per genus et differentiam)  Step 3: Synthesis  Step 4: Standardization  Step 5: Ordering  Following the above steps leads to the creation of a set of facets. They constitute a faceted representation scheme for a domain 21

22. Ontological Principle Relevance (e.g.,breed is more realistic to classify the universe of cows instead of by grade) Ascertainability (e.g., flowing body of water) Permanence (e.g., Spring- a natural flow of ground water) Exhaustiveness (e.g., to classify the universe of people, we need both male and female) Exclusiveness (e.g., age and date of birth, both produce the same divisions) Context (e.g., bank, a bank of a river, OR, a building of a financial institution) Important: helps in reducing the homographs Currency (e.g., metro station vs. subway station) Reticence (e.g., minority author) Ordering Important: ordering carries semantics as it provides implicit relations between the coordinate terms 22

23. Identification of the atomic concepts  Sources of the concepts  WordNet  GeoNames  TGN  Literature 23

24. Identification of the atomic concepts  Some of the relevant sub-trees in WordNet are:  location  artifact, artefact  body of water, water  geological formation, formation  land, ground, soil  land, dry land, earth, ground, solid ground, terra firma Note: not necessarily all the nodes in these sub-trees need to be part of the space domain. For example, the descendants of artifact, like, article, anachronism, block, etc. are not. 24

25. HillStream River the well defined elevated land formed by the geological formation (where geological formation is a natural phenomenon) altitude in general >500m the well defined elevated land formed by the geological formation, where geological formation is a natural phenomenon altitude in general <500m a body of water a flowing body of water no fixed boundary confined within a bed and stream banks a body of water a flowing body of water no fixed boundary confined within a bed and stream banks larger than a brook Mountain Analysis 25

26. Body of water Flowing body of water Stream Brook River Stagnant body of water Pond Landform Natural depression Oceanic depression Oceanic valley Oceanic trough Continental depression Trough Valley Natural elevation Oceanic elevation Seamount Submarine hill Continental elevation Hill Mountain * each term in the above has gloss and is linked to synonym(ous) terms in the knowledge base Synthesis 26

27.  Space [Domain]  by geographical feature [Entity class]  by water formation  by land formation  by land  by administrative division  …  by relations [Relation]  spatial relation  direction, internal, external, longitudinal, sideways, etc.  functional relation (e.g., primary inflow, primary outflow)  …  by attribute  [Datatype attribute]  latitude  Longitude  dimension  …  [Descriptive attribute]  Natural resource  Architectural style  Time zone  ph  History  … Facets and sub-facets 27 Log-in: http://uk.disi.unitn.it/resources/html/UKDomain.html

28. References  F. Giunchiglia and B. Dutta. DERA: A Faceted Knowledge Organization Framework. Technical report, KnowDive, DISI, University of Trento, 2010.  B. Dutta, F. Giunchiglia, V. Maltese, A facet-based methodology for geo- spatial modelling, GEOS, 2011.  Crawford, J. M. & Kuipers, B. (1990). ALL: Formalizing Access Limited Reasoning. Principles of semantic networks: Explorations in the representation of knowledge, Morgan Kaufmann Pub., 299-330.  S. R. Ranganathan. Prolegomena to Library Classification. Asia Publishing House, 1967.  T. R. Gruber. A translation approach to portable ontologies. Knowledge Acquisition, 5(2):199-220, 1993.