Logics for Data and Knowledge Representation The DERA

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

Knowledge Representation (KR) q Abstraction of the world via models, of a particular domain or problem, which allow automatic reasoning and interpretation q Fundamental q to Goal 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

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

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

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

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

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

Domain q. A domain can be decomposed into its several constituents, and q Each of them denotes a different aspect of entities q 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, … q Each 8 of these aspects is called facet

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

DERA qa facet based knowledge organization framework q independent from any specific domain q allows building domain specific ontologies q mapping to Description Logic q logically sound q decidable q Developed 10 by the Uni. Tn Know. Dive group

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

Entity (E) q 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 = q Where, q. C = a set of entity classes or concepts representing the entities q E' = a set of entities (also called objects, instances or individuals), possibly, real world named entities, those are the instantiations of C 12

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

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

Entity (E) An example from the Space domain 15

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

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

Attribute (A) (contd…) q Datatype Attributes (A'): q The datatype attributes include the attribute classes that account the quality or quantity of an entity within a domain q E. g. , q latitude, q 450 N, 180 S q altitude q q q 18 (of a mountain): 8000 ft, 2400 m. high, low q depth q longitude (of a place): (of a lake): deep, shallow 100 ft. , 20 m.

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

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

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

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

Identification of the atomic concepts q Sources of the concepts q Word. Net q Geo. Names q TGN q Literature 23

Identification of the atomic concepts q Some q q q of the relevant sub-trees in Word. Net 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

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

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

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

References q F. Giunchiglia and B. Dutta. DERA: A Faceted Knowledge Organization Framework. Technical report, Know. Dive, DISI, University of Trento, 2010. q B. Dutta, F. Giunchiglia, V. Maltese, A facet-based methodology for geospatial modelling, GEOS, 2011. q 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. q S. R. Ranganathan. Prolegomena to Library Classification. Asia Publishing House, 1967. q T. R. Gruber. A translation approach to portable ontologies. Knowledge Acquisition, 5(2): 199 -220, 1993.