1. Representation without Reason: Slow Progress toward the Semantic Web Jim Greer ARIES Laboratory Computer Science, University of Saskatchewan

2. The Attraction of Ontologies  Shared meanings  Nice formal representation  Sound reasoning facility  Once built, they remain stable  Building an ontology brings deep understanding and requires reflection

3. Concept Maps and Taxonomies  Historically useful in education  Learning is strengthened by constructing concept maps  Social construction of knowledge  A small leap from taxonomy to ontology?

4. Formal models and meaning  Semantics through links, rules, and propagation  RDF triples for micro-content  Foundation of our MUMS system  Aggregation and abstraction  Our early work on granularity

5. Indexing content  Ontologies are convenient to use  Simple representation  Trivial inference needed  Propagation through link semantics  Natural to attach metadata  But can we all agree? Must we?  Do we need more than taxonomies?

6. Our Ontology work  Debate over “the” representation  Domain concepts are fluid  Fall back to concept mapping  Semantics weaken  Top-down reasoning vanishes  Resort to folksonomies/data-mining

7. Tempting E-Learning Illusions  Concept maps => Ontologies  Teachers / learners can understand ontologies  Teachers, learners and machines have a common understanding of an ontology  Users will embrace ontologies  Easy to build an ITS once the ontology is right

8. “My own” ontology  Formal modelling tool based on consensus  Gaining popularity in MDA (formal specification)  Shared meanings in a small closed community  My ontology is better than yours!

9. Ontology mapping  Translate one ontology to another  Appealing notion if no agreement can be reached  Tougher than it looks…

10. All-too-common use case  Ontology is built by a group with much effort  Every user wants to tweak the ontology  Ontology becomes primarily a representation tool (taxonomy)  No sophisticated reasoning happens

11. Is there enough benefit?  Why is the semantic web proceeding so slowly?  Where did the agents go?  Are ontologies really promoting interoperability?  How much prototyping and informal modelling is needed prior to building an ontology?  What does the ontology really do for learners?

12. Jim’s SWEL Challenge  Tools for the emergent ontology  Learning an ontology from associations  Substantial reasoning with ontology  Use cases where reasoning is key  Make ontologies truly useful  Too many people “can’t be bothered”  Formal structures must pay off.