1. Ontology Technology applied to Catalogues Paul Kopp

2. Paul KoppWGISS22Annapolis 2 About Descriptive Logics ■Descriptive Logics is a formalism for representing knowledge ■Knowledge concerns concepts, roles and individuals  An individual is in relationship with at least some concept  Example  “Aristotle” is an individual  “human being” is a concept  “is-a” is a relation  The individual “Aristotle” is in the relationship “is-a” with the concept “human being”  A concept is actually a set of individuals  Concepts may be built by applying operations to atomic concepts  Examples: restriction, intersection, union  A role is a relationship between two concepts  Example “hasChild” is a role expressing the relationship between the concepts “parent” and “child”  A role is actually a relationship between individuals  Constructions and Restrictions may apply to roles as well (ex.: intersection, “≥3hasChild”)

3. Paul KoppWGISS22Annapolis 3 About Descriptive Logics (continued) ■Knowledge Representation Systems based on Descriptive Logics  Descriptions  TBox (Terminology Box) –Contains the terminology given as concepts, roles and constructs on them –Ex.: (a) “satellite” “is-a” “space engine” –A TBox can contain complex descriptions, depending on the constructors that are available (i.e. on the definition of the Description Language)  ABox (Assertion Box) –Contains assertions about individuals in relationship with the terminology –Ex.: “SPOT5” “is-a” “Satellite” (SPOT5 is an individual)  Reasoning Services  From the TBox one infers properties of descriptions –Satisfiability, Subsumption, Equivalence, Disjointness  From the ABox, one infers properties of assertions w.r.t. the TBox –Consistency, Retrieval (find all individuals that are instances of a given concept), Realization (find the most appropriate concept for a given individual)  Complexity of reasoning is a major issue –Rich Description Languages entail complex reasoning (with possible untractability of some inferences like subsumption)

4. Paul KoppWGISS22Annapolis 4 About Descriptive Logics (continued) ■Comparison with other Knowledge Representation Formalisms  Conceptual Graphs (Sowa)  Semantic Data Models  Entity Relationship Model (Chen)  Conceptual Modelling  Unified Modelling Language (Rumbaugh) Specialists have been studying the correspondences between all these formalisms.

5. Paul KoppWGISS22Annapolis 5 About ontologies ■From ontos (Greek οντοσ = “which is real”) and logos (Greek λογος = “word”, “speech”) ■Name given to knowledge representations where the main relationship is the “is-a” relationship ■Ontologies are used to describe the concepts that prevail in a domain ■Thesauri are very simple ontologies  Excerpt from the IDN keywords: ATMOSPHERE >ATMOSPHERIC WATER VAPOR >EVAPOTRANSPIRATION ATMOSPHERE >ATMOSPHERIC WATER VAPOR >EVAPOTRANSPIRATION ■Tools to create ontologies  Racer (http://www.racer-systems.com)http://www.racer-systems.com  Stands for Renamed ABox and Concept Expression Reasoner  Protege (http://protege.stanford.edu)http://protege.stanford.edu  SWOOP (http://www.mindswap.org/2004/SWOOP)http://www.mindswap.org/2004/SWOOP ■Reasoners  Pellet (http://www.mindswap.org/2003/pellet)http://www.mindswap.org/2003/pellet  Racer

6. Paul KoppWGISS22Annapolis 6 Ontologies and the W3C ■Web Ontology Language (OWL)  Defined by the W3C  Specification of ontologies using the xml/RDF schema  Concept = class in OWL  Role = property in OWL  3 levels of expressiveness  OWL-Lite (for simple ontologies like thesauri)  OWL-DL (for ordinary Descriptive Logics compliant ontologies)  OWL-Full (no computational guarantee) ■SPARQL Query Language for RDF  Defined by the W3C  Specification of queries on ontologies specified with OWL ■OWL and SPARQL implemented in several ontology tools (ex.: Protege)

7. Paul KoppWGISS22Annapolis 7 Ontologies and Catalogues ■Catalogue primary entries are metadata ■Traditional metadata retrieval  Queries are applied to predefined “queryable” metadata elements (title, keyword, etc.)  Metadata satisfying the query are retrieved from the database and presented to the user (full or predefined “brief” or “short” content) ■Another way to retrieve metadata?  Express metadata as ontologies  Link the metadata expressed as ontologies to a reasoner  Make the reasoner available to the catalogue user  The catalogue user may ask any question he wants to the catalogue

8. Paul KoppWGISS22Annapolis 8 Experiment at CNES ■Extension of the CNES ISO19115 Metadata Catalogue  Developed under the auspices of CNES R&D (project R-S06/OT-0005-013)  Metadata are inserted as ordinary xml files  Metadata may also (additionally) be inserted as ontologies (OWL-DL)  The catalogue user queries the catalogue as usual (keyword, time, location, etc.)  The catalogue user may ask for “more queries”  The catalogue system opens a reasoner (Pellet, through its Java API)  The user prepares a query from predefined SPARQL templates (the user just enters the values for the query variables)  SPARQL templates are prepared by the Catalogue Manager  Results from the “more queries” function are merged with the previous ones and presented to the user ■End of development expected in 4Q06

9. Paul KoppWGISS22Annapolis 9 Thank you!