1. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU1 Ontology Construction & Tools Atilla ELÇİ Dept. of Computer Engineering Eastern Mediterranean University

2. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU2 Ontology Development The Domain Expert’s Expressway:  Ontology Development 101: A Guide to Creating Your First Ontology by Natalya F. Noy and Deborah L. McGuinness. Ontology Development 101  Tools used: Protégé with OntoViz API.  Note that: (i) extensive domain knowledge, and (ii) ontology tools skill are required for building usefull ontologies.  Example: Brusa et al: A Process for Building a Domain Ontology, AOW 2007.AOW 2007

3. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU3 Ontology Development Through Knowledge Discovery The (Syntactic) Discovery Approach [Davies et al. Ch. 2]: Knowledge discovery Ontology definition Semi-automatic ontology construction Ontology learning scenarios Knowledge discovery for ontology learning

4. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU4 Knowledge Discovery  Knowledge discovery: developing techniques enabling automatic discovery of novel and interesting information from (raw) data.  Lately, un-/semi-structured domains, such as: Text Mining, Web Mining, Link Analysis (graphs/networks) Relational Data Mining (relational / first order form) Stream Mining (analysis of data streams)... are of interest. => Semi-Automatic Ontology Construction

5. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU5 Knowledge Discovery (continued)  KD relates to such research areas as: Computational Learning Theory: theoretical questions about learnability, computability, learning algoriths. Machine Learning: automated learning and knowledge representation Data Mining: using learning techniques on large-scale real-life data, Web Mining, Statistics-cum-Statistical Learning: techniques for data analysis.  Conference: 9th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2007), Sept. 3-7, 2007, Regensburg, Germany. Proceedings in LNCS.DaWaK 2007  CFP due date:  Submission of abstracts: April 2, 2007  Submission of full papers: April 13, 2007  Check KD subjects.  DaWaK 2008 DaWaK 2008

6. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU6 Ontology Definition  Ontology is a graph / network structure consisting of: A set of concepts (vertices in a graph) A set of relationships connecting concepts (directed edges in a graph) A set of instances of a particular concept or relationship (data records).  Formal/theoretical definitions of ontology as an abstract structure: Ehrig et al. (2005): based on similarity measure Bloehdorn et al. (2005): through integration of MLs.

7. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU7 Ontology Engineering Semi-Automatic Ontology Construction  Ontology Life Cycle of DILIGENT ontology engineering and construction methodology: building, local adaptation, analysis, revision, and local update.  Semi-automatic ontology construction (a la CRISP-DM ‘data mining’ methodology): 1. Domain understanding: interest area. 2. Data understanding: data versus semi-automatic ontology construction. 3. Task definition: tasks of interest that are doable with the available data. 4. Ontology learning: semi-automatic process executing the tasks of step 3. 5. Ontology evaluation: estimating quality of solution to taks. 6. Refinement (semi-/manual): human-in-the-loop transformation to improve the ontology. Business Domain Ontology Domain

8. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU8 Ontology Learning Scenarios  Typical ones are as follows: Inducing concepts and clustering of instances (given instances) Inducing relations (given concepts and instances) Ontology population (given an ontology and relevant but not-associated instances) Ontology generation (given instances and background info) Ontology updation (given an ontology and new instances).

9. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU9 Knowledge Discovery for Ontology Learning  KD aims to extract a structure in the data. That is, mapping unstructured data into ontological structure.  At the same time, keep in mind scalability issues as KD process is used necessarily on real-life dataset volumes (~terabytes).  Some KD techniques used in addressing the ontology learning scenarios: Unsupervised Learning Semi-Supervised, Supervised, and Active Learning Stream Mining & Web Mining Focused Crawling Data Visualization

10. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU10 Unsupervised Learning  By grouping like instances through comparing them against each other and suggesting labels for the groupings that evolve. Methods used are: Document Clustering Latent Semantic Indexing  Ref. Section 2.6.1.

11. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU11 Semi-Supervised, Supervised, and Active Learning  Man-in-the-loop, tools-assisted approaches  Reference Section 2.6.2

12. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU12 Stream Mining & Web Mining  Stream mining: schemes for rapidly changing data running continuously.  Web mining: Web content mining Web structure mining Web usage mining  Reference Section 2.6.3.

13. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU13 Focused Crawling  The approaches dealing with collecting documents on the Web.  Reference Section 2.6.4.

14. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU14 Data Visualization  For obtaining early measures of data quality, content, and distribution.  Reference Section 2.6.5

15. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU15 Further References on Ontology Construction  Reference Section 2.7.  Especially note Fernandez (1999) paper on analyzing ontology development approaches against IEEE Standard for Developing Software Life Cycle Processes.  Reference Section 2.8: Note hints on research directions.

16. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU16 Ontology Development Tools  Ontology Tools Survey, Revisited by Michael Denny Ontology Tools Survey, Revisited  W3C Semantic Web Tools Wiki pageSemantic Web Tools

17. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU17 Commercial SemWebTech Conferences  Semantic Technology Conference (SemTech 2007 ), 20-24 May, 2007, San Jose, California, USA. A PDF of the conference brochure is available for download at the conference website.SemTech 2007conference website.  DAMA Intl Symposium & WILLSHIRE Meta-data Conference, 4-8 March, 2007, Boston, MA, USA. Download the Full Conference Program and Brochure in PDF Here (1.3 mb). Other Willshire Conference tracks. DAMA Intl Symposium & WILLSHIRE Meta-data Conference Download the Full Conference Program and Brochure in PDF Here (1.3 mb)Conference tracks

18. 13/03/'07 upd 11/03/08CmpE 588 Spring 2008 EMU18 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. 2.: pp. 9-25.  Brusa, G., Caliusco, M.L. and Chiotti, O. (2006). A Process for Building a Domain Ontology: an Experience in Developing a Government Budgetary Ontology. In Proc. Second Australasian Ontology Workshop (AOW 2006), Hobart, Australia. CRPIT, 72. Orgun, M.A. and Meyer, T., Eds., ACS. 7-15  Ontology Tools Survey, Revisited by Michael Denny (published July 14, 2004 on xml.com) along with Michael's famous Ontology Editor Survey 2004 Table. Ontology Tools Survey, RevisitedOntology Editor Survey 2004 Table  W3C Semantic Web Tools Wiki page:Semantic Web Tools Check Jena, SemWeb, Protégé, Swoop, etc.