Developing Ontologies for Knowledge Management

1. Developing Ontologies for Knowledge Management Atilla ELÇİ Dept. of Computer Engineering Eastern Mediterranean University CmpE 588 Spring 2008 EMU

2. Knowledge Management Topics • Motivation • Terms & Definitions • Roles of ontologies • PROTON ontology as bases for KM / SemWeb Apps CmpE 588 Spring 2008 EMU

3. Motivation • Knowledge Representation (KR): = A world view: Building models of a domain/problem which allow for automatic reasoning and interpretation. => Formal semantics (Ontology!) => Machine-interpretable meaning • Semantic repository: Storage, querying, and management of structured data • DBMS vs Ontology-based • O-B provides depth of meaning not available through DBMS CmpE 588 Spring 2008 EMU

4. Terminology: KM views • What Is Knowledge Management by the The Knowledge Management Forum (KMForum): • Read through these personal views on K & KM • Note the diversity of views & interests • Contrast & cross-check definitions of some viewers. CmpE 588 Spring 2008 EMU

5. Terminology • Dublin Core Metadata Initiative (DCMI, DC): interoperable online metadata standards • Dataset: a set of structured data (list, table, DB, etc.) useful for direct software processing • Ontology: • = Paradigm for KR in AI. • Conceptual schemata • Formal ontology as logical formalism as in OWL • Schemata or ‘inteligent’ views over information resources: • For indexing, querying, and referencing non-ontological datasets • For DB, Document Mngt Sys, Catalog, OLAP, CmpE 588 Spring 2008 EMU

6. Terminology (continued) • Ontology classification based on generality of conceptualization: • Upper-level ontology: A general model suitable for large variety of tasks, domains, and application areas. Can be used to line up independently developed ontologies if linked to it. • Domain ontology: For ‘specific’ domain of interest • App / Task ontology: For a specific range of applications / tasks. • Knowledge base (KB): • A dataset with formal semantics and knowledge representation allowing automatic inference. • Ontology: O=<C, R, I, A> where: • C: is the set of classes • R: is the set of relations among the classes • I: is the set of instances from the domain. Instances belong to classes • A: is the set of axioms (say, business rules). CmpE 588 Spring 2008 EMU

7. Terminology (continued) • Ref ontology definition as O-grammar, the issue of what is instance what is schema definition may not always easily resolved. • Data qualia: A data quale is an orthogonal quality of data that may be used for independent classification: • Semantics: whether it is formally represented • Structure: if the data is formally structured • Schema: data that determines shape and/or meaning of ontology data. • Sorts of data (“_” stands for ‘any value’ not determined): • Data: (_,_,_), ie. Any sort of collection of data • Dataset: (_,structured,_) • Knowledge Base: (semantic,structured,_) • Ontology: (semantic,structured,schema) • Non-semantic schemata: (nonsemantic,structured,schema) • Database: (nonsemantic,structured,schema) • Mixed datasets: (_,structured,schema&non-schema) • Content: (_,non-structured,_) • Metadata: data on data, annotation, ... How to represent in (?,?,?)? • Semi-structured data: CmpE 588 Spring 2008 EMU

8. Terminology (continued) • Sorts of data (continued): • Semi-structured data: • KR/NLP  Docs containing free text fragments in structured according to some schema • DB  Data of non-relational data model. Ref. Fig. 7.2- Structured vs semantic positioning of various sorts of data. CmpE 588 Spring 2008 EMU

9. Roles of Ontologies • Ontology as Database Schema: • May not contain instance data. • Such as RDBMS schema. • Ontology as Topic Hierarchy: • Classification for various purposes: • DCMI and library classification • Yahoo & DMoz taxonomies for Web data • See Section 4 in this for depth of Yahoo! Directory. • Compare Topic-Ontology versus Schema-Ontology (Sect. 7.5) • Ontology as Enterprise Resource Model: • Ref.: Ontolog Database & Ontology Mini-Series. CmpE 588 Spring 2008 EMU

10. Mapping & Querying Disparate Knowledge Bases • Self study: Davies §6.3 CmpE 588 Spring 2008 EMU

11. PROTON (PROTo ONtology) Ontology • A light-weight uppper-level ontology to serve as model bases for information science community for, for example: • Seed for ontology generation • Automatic entity recognition & information extraction • Metadata generation / semantic annotation. • Design Rationale: • For usage in KM & SemWeb appls • Light-weight: for being unrestrictive • Prefers not to deal with time & space • Low-cost of adoption & maintenance • Scalable reasoning CmpE 588 Spring 2008 EMU

12. PROTON (contiuned) Consists of ~300 classes & 100 properties for: • Semantic annotation • Indexing, and retrieval. • Design principles: • Domain independence • Light-weight logical definitions • Alignment with popular metadata standards • Good collection of named entity types (people, organizations, locations, numbers, dates, addresses. • Structure: • In OWL Lite • In four modules: System, Top, Upper, and Knowledge Management (KM) • Organized á la DILIGENT Methodology, CmpE 588 Spring 2008 EMU

13. PROTON (contiuned) • Scope: • Developed in the SEKT Project through sampling of a corpus of general news. • General entity types appearing commonly (Person, Location, Organization, Money, Date, ...) are in PROTON Top. • KM aspects stems from: • KIMO of KIM Project • OpenCyc • Wordnet • DOLCE • EuroWordnet • Voluntary compliance with: • Dublin Core • Automatic Content Extraction annotation types • Alexandria Digital Library Feature Type Thesaurus • Future compliance with: FOAF and other popular standars & ontologies. CmpE 588 Spring 2008 EMU

14. PROTON (contiuned) • Architecture: • Site at Semanticweb.org • Organized in three levels: Basic, Top, Upper • In four modules: • System (basic; protons:...): application ontology meant for use by ontology-based software • Top (top; protont:...): abstractions • Upper (upper; protonu:...): specific cases • KM (upper; protonkm:...): specific cases CmpE 588 Spring 2008 EMU

15. PROTON (contiuned) • KM module: for application-specific extension of PROTON: • Information Space:collection of themed info resources • Software Agent: specialized Agent • User: User and UserProfile • Profile • User Profile • Mention: name droppings, references to (private) instances • Weighted Term: relates objects to numbers • Device: references to user devices. CmpE 588 Spring 2008 EMU

16. Organizations • The Knowledge Management Forum (KMForum) • Virtual community of practice focused on furthering fundamental theories, methods and practices. Features archives and news. • What Is Knowledge Management • KM Forum • Boston Knowledge Management Forum: A Community of Practice: Learning and Working in the Knowledge Management Community • KnowledgeBoard • Forum to establish a community and to support and identify commonality in terminology, application and implementation. Features news, workshops, a library, ... CmpE 588 Spring 2008 EMU

17. Conferences • Knowledge Representation Ontology Workshop (KROW 2008). • Eleventh International Conference onPrinciples of Knowledge Representation and Reasoning (KR 2008), • Sydney, Australia, September 16 - 19, 2008 CmpE 588 Spring 2008 EMU

18. Commercial Conferences • Knowledge Base Publishing course series of the Montague Institute includes articles: • Introduction to Knowledge Base Publishing • Taxonomies, search & Sharepoint • Metadata and search • Integrating taxonomies • Information modeling and metadata management • See also Roundtables, for example the following: • Benchmarking Sharepoint for KM (December 12, 2007) • Six weeks to the Semantic Web (November 7, 2007) • Integrating folksonomies with Google (October 17, 2007) • Migrating metadata to the Semantic Web (September 5, 2007) CmpE 588 Spring 2008 EMU

19. References • John Davies, Rudi Studer, Paul Warren (Editors): Semantic Web Technologies: Trends and Research in Ontology-based Systems, John Wiley & Sons (July 11, 2006). ISBN: 0470025964. Ch. 7.: pp. 115-138. • W3C Semantic Web Tools Wiki page: • Check ... CmpE 588 Spring 2008 EMU