1. Semantic Data & Ontologies CMPT 455/826 - Week 5, Day 2 Sept-Dec 2009 – w5d21

2. Comparing Relationships in Conceptual Modeling: Mapping to Semantic Classifications (Based on Veda C. Storey) Sept-Dec 2009 – w5d22

3. Classifying verb phrases Approaches to classifying verb phrases: –recognize common, generic verb phrases –highlight the importance of data abstractions in conceptual modeling –identify the need to deal with the domain-dependent nature of relationships. Sept-Dec 2009 – w5d23

4. An Ontology is a way of describing one’s world generally consists of: –terms, –their definitions, and –axioms relating them Sept-Dec 2009 – w5d24

5. Storey’s Semantics Total of 24 categories “Although there may be no perfect way to neatly divide verb phrases into predetermined dimensions, –there have been useful attempts to develop exhaustive classifications of verbs of the English language “The best interpretation of many verb phrases –depends upon the purpose and context within which a verb phrase occurs and the nouns that surround it Sept-Dec 2009 – w5d25

6. Storey’s Semantics ClassificationExplanationExample ChangeBecome differentStudent alters timetable CommunicationPass on, transmitProfessor lectures students CompetitionCompete for something, measure against others Student competes for honours ConsumptionGet through, useStudent exhausts loan ContactReach, get in touch withStudent notifies professor Cognition or perceptionUnderstanding, knowledge, becoming aware Student understands guidelines Sept-Dec 2009 – w5d26

7. Storey’s Semantics (2) ClassificationExplanationExample CreationMake, cause to beProfessor makes assignments MotionMoves, ridesAssistant transfers textbooks PossessionOwn, have as an attribute, knowledge, skill Professor owns copyright Social interactionInteraction to carry out socially accepted business operations Employee hired by university Transaction / exchange trade Involving the activities of a business transaction Students buy books Evaluate or observeWatch, find, discover, notice Professor assesses student performance Sept-Dec 2009 – w5d27

8. Analyzing Storey’s “Ontology” It consists of: –terms, –but not proper definitions, and –no axioms relating them Storey’s Terms –are not an extension of previous semantic modeling paper Storey’s “Explanations” –often define a term in terms of itself e.g. Transaction –sometimes define two terms with the same concept e.g. Knowledge Sept-Dec 2009 – w5d28

9. The results Beware of jumping on the bandwagon of the latest trend (e.g. Ontologies) without –tying the new concepts to existing proven concepts –understanding and fully complying the new trend The latest paper by an author is not necessarily the best We need to know more about ontologies –in order to determine how we might apply them Sept-Dec 2009 – w5d29

10. Data modelling versus Ontology engineering (Based on Spyns, Mersmann, & M. Jarrar) Sept-Dec 2009 – w5d210

11. Data Models & Ontologies Data models –represent the structure and integrity of the data elements of a single” specific enterprise’s application(s) –define a conceptualisation and vocabulary not intended a priori to be shared by other applications The fundamental asset of ontologies –is their relative independence of particular applications an ontology consists of relatively generic knowledge that can be reused by different kinds of applications/tasks Sept-Dec 2009 – w5d211

12. Ontologies An ontology contains –the vocabulary (terms or labels) –the definition of the concepts –their relationships for a given domain In many cases –the instances of the application (domain) are included –as well as domain rules that are implied by the intended meanings of the concepts Sept-Dec 2009 – w5d212

13. So what are Ontologies This paper seems to contradict itself –regarding the distinction with data models –by using the terms “application” and “domain” without defining them –Application is used to refer to an organization’s specific system for the application –Domain is used to refer to the more global concepts shared by a number of similar applications across many systems and organizations The real difference is the focus –on sharing application/domain data between different systems Sept-Dec 2009 – w5d213

14. A Faceted Approach to Building Ontologies (Based on Prieto-Díaz) Sept-Dec 2009 – w5d214

15. Building an Ontology A terminology is first developed –providing a controlled vocabulary for the subject area or domain of interest, then it is organized into a taxonomy –where key concepts are identified, and finally these concepts are defined and related –to create an ontology Sept-Dec 2009 – w5d215

16. Taxonomies A taxonomy is a structure of categories Classification is the act of assigning entities –to categories within a taxonomy Sept-Dec 2009 – w5d216

17. Taxonomies vs Ontologies A taxonomy –“a controlled vocabulary which is arranged in a concept hierarchy” An ontology –“a taxonomy where the meaning of each concept is defined by specifying properties, relations to other concepts, and axioms narrowing down the interpretation.” Sept-Dec 2009 – w5d217

18. Classification Schemes A classification scheme must be able to –express hierarchical relationships, as well as –relationships created to relate two or more concepts belonging to different hierarchies Hierarchical relationships –are based on the principle of subordination or inclusion and –are typical in a taxonomy Relationships among concepts –are presented as compounded classes. Sept-Dec 2009 – w5d218

19. Approaches to Classification The enumerative (or traditional) method –(top down organization) –postulates a universe of knowledge –divided into successively narrower classes –that include all possible subclasses and compounded classes The faceted approach –(bottom up organization) –relies on building up or synthesizing facets, which are –conceptual clustered groups of elemental classes –(there are multiple possible facets rather than a single hierarchy) Sept-Dec 2009 – w5d219

20. On Using Conceptual Data Modeling for Ontology Engineering (based on Jarrar, Demey, & Meersman) Sept-Dec 2009 – w5d220

21. Conceptual Schemes & Ontologies Conceptual schemes (e.g. ER models) –are intended to be used during design phases and not at the runtime of applications they are the basis for SQL which is used at runtime –are developed only for the use of an enterprise application(s) Ontologies –are typically used and accessed at runtime requiring computability –are supposed to hold application-independent domain knowledge Sept-Dec 2009 – w5d221

22. So what does this all mean? Ontologies generally consists of: –terms (that are chosen to represent conceptual entities) –their definitions (so that synonyms can be recognized) –axioms relating them (which need not be hierarchical) Ontologies focus on –domains rather than individual organization’s implementations –sharing application/domain data between different systems Ontologies –are more than just an analysis and design aid –should be able to be used and accessed at runtime Sept-Dec 2009 – w5d222

23. And what should we do about it? How can ontologies be used? –to structure data within an application / database –to structure and find data in a data warehouse –to support communications between applications –to provide guidance for integrating data How can components of ontologies be used? –to identify metadata (terms) –to identify criteria and constraints (definitions) –to identify relationships and interactions (axioms) Sept-Dec 2009 – w5d223

24. And what more? Looking within applications and databases –consider how they might relate to triggers identifying event deciding on appropriate actions taking actions If we identify how we want to use them –we can identify requirements for developing them –so that they meet our needs –Merely developing ontologies for their own sake, is not enough! Sept-Dec 2009 – w5d224