1. “D.A.I. & S.M. for KM” a synergy of complementary domains and challenges  the semantic web addicted people “please, raise your hands !”

2. Profiles and interests of participants ? “knowledge manager, machine learning and dynamic construction of knowledge, web-services and DAMLS, e-mail and SW for KM, information retrieval, constraints, standard upper ontologies, corporate memories, linguist, semantic intraweb, peer two peer for KM, ontology for processes and interaction protocols, etc.”

3. What is new in the semantic web ? –Other K.R. languages existed before but none of them made it to the real world ; some part also matured (ontology) –SW is a real-world application (the Web) for K.R. –the SW is also about standardization and diffusion effort for semantic representation on the Web. What is new in the agents ? –High level programming and design paradigm that reduces conceptual gap between description of our reality (problems and envisioned solutions) and the description used in the modeling and implementation framework. Why Agents & SW interesting in KM? –Distributed A.I. offers a paradigm and architectures to deploy and map over distributed knowledge spaces –Virtual organizations can reflect, and integrate with human organizations

4. Why do we think there exists such a thing as an ontology? –The use of abstract categories shows up in a lot of work –XML is not enough, the machine does not understand any more than “car” ; need for ontologies and SW –True both for the open Web and for the intrawebs –Even if the human brain representation is completely different of the ones (D)AI is using, if our symbolic systems can simulate the inferences we want using ontologies then why not use them? Ontology problem: the heart of SW and symbolic DAI –Contrary to previous attempts, the “ontology” object and its problematics are recognized and being addressed. –There is an effort in trying to build standard top ontologies (SUMO), and domain ontologies that can be reused and extended by organizations. –No imposed standards, make them available and show benefits to everyone ; otherwise it will not happen

5. Importance of the content of ontologies and SW? –Semantic content or statistic content? –A lot of low-quality ontologies on the Web but they will disappear with time / hope they won’t harm the domain –The linguistic / semiotic level is too often mixed-up with the conceptual structures and representation themselves ; need for separation and development of this level. –Problem of pragmatic use of terms / signs and interpretation not really addressed –Content and semantic are largely underestimated, tools and methods are too much emphasized –You have to go through a period of chaos before you reach a stable situation –Transition period: going to double web before going where everything is in the markup. –SW initiatives also provide rules, constraints on how it should be done i.e. it is more than a simple syntactic sugar

6. Can large, standard ontologies exist? –“Build small but viral” Tim Burners-Lee –80/20 rule for dissemination Let the demand for the rest come after –Choose the right domain to build and demonstrate ontologies (e.g., services, processes, interaction protocols) –Then tend toward a maximum of expressiveness and overlap with other existing ontologies –Top ontologies and standard domain ontologies are vital to foster this convergence and make the compatibility possible. Extensible models are important because they give room for further extensions –Layers? The semantic web cake. –Components? But too much anarchy would be dangerous. –Top ontology (essential) + hierarchies of extensions and management of overlaps between extension –Semi-automatic mapping for relevant parts

7. Is there a killer-application for the SW? –Exact answer to my query? Improving search mechanisms? –Mechanisms to reduce number of answer? And what if there really are 1,000,000,000,000,000 answers –Real improvements? Not ambiguity. –Only as good as the expressivity of the ontology. –Need more weighting / fuzzy ? No, just sub-type of Ont. K. –Pornography ? –Hmm let say… “Multimedia” May be look at trust, quality and security: –Use formal knowledge to evaluate some quality (e.g., coherence) and security (e.g. access policies) –Use for filtering and ranking –Some solution of K.M. (e.g., peer review, trust authorities and (acquaintance) networks)

8. AMKM and SW –Large organizations with intranet = interesting special case –Intraweb application are a good domain of application (information systems and workflow) –Problem of burden, separation of concerns in the company (worker vs. K Manager) SW : get KM outside the organization ; helps link with open web and link with other organizations. –Virtual enterprises –Company merging Designing shared common ontology –Corporate internal ontologies –Top ontology ex: SUMO then extension with domain ontologies Ontological work in the agent field can bring works on speech acts and interaction protocols (FIPA, KQML) to SW and KM

9. Complexity of ontologies –“Too complex to be shown to a user” –No reason to show it to a user Interfaces are a very important problem –Forms are not usable for every interactions –More intelligent interfaces using semiotic levels –“We focus, and interfaces should focus with us” –Pragmatic aspects of language in interfaces Who is going to give us this semantic that the SW wants to make available? –Some of it manually (e.g. building an ontology) –Some of it from (semi-)automatic process –Pragmatic aspect of the interpretation of the content of the Web

10. DAI SW Ontology K.M. Speech acts, ACL, message primitives Natural paradigm, Distributed platforms and frameworks Standard frameworks and languages Online libraries and standard ontologies Frameworks and languages for semantic-level message interactions Intraweb = good application domain and testbed K typologies, K life-cycle methods and tools K A/R

11. DAI SW Ontology K.M. Distributed archi. (CSCW) for emergence maintenance and use of ontological consensus Good application domain ; Studies of organizational knowledge and its dynamics Standard for intra and inter enterprise exchanges Modeling primitives, ontology engineering methods for SW schemata Ontology-based KM platforms Knowledge reuse and standard ontologies Distributed archi. maintenance and use of assertional knowledge