1. Developing Semantic Web Sites: Results and Lessons Learnt Enrico Motta, Yuangui Lei, Martin Dzbor, Vanessa Lopez, John Domingue, Jianhan Zhu, Liliana Cabral, Alex Goncalves, Victoria Uren

2. Motivation for KMi Sem Web Key Objective To generate a live, declarative representation of what happens in KMi, which can support smart queries and the specification of intelligent services producing smart inferences on the basis of this data Initial version was ready in 1998 PlanetOnto System (95-98)

3. Relates-event PeopleProjectOrganizationTechnology Event Story

6. Story Database NewsBoy NewsHound Modelling Language (OCML) Planet KB KA Tool Email Query Interface Planet Ontology Web Browser WebOnto Architecture of Planet-Onto

7. Story Database NewsBoy NewsHound Modelling Language (OCML) Planet KB KA Tool Email Query Interface Planet Ontology Web Browser WebOnto Architecture of Planet-Onto

8. Key Criteria for Sem Web Site Emphasis on Automatic KA Fully automated generation of information No knowledge capture bottleneck Manual annotation is welcome but should not be a core part of the process Manual annotation should not require sophisticated KR skills Ideally manual annotation should take place through side effects generating from normal work activities Architecture Keep the semantic layer separated (and to some extent independent) from the actual web site Interoperability Semantic Web Site ought to be open Semantic representation publicly available to any reasoning engine who wants to use the information

9. DBs Mapping Specs KMi Semantic Web Site Docs XML mark-up Mapping Engine Domain ontology Raw KB Data Verification Engine KB Source DataIntegration LayerVerification LayerTarget Data Information Extraction Engine (Espotter)

10. Ontological Structure KMi Semantic Web KMi Ontology AKT Portal Ontology AKT Support Ontology AKT Reference Ontology Publications Projects Research Areas People Organizations Technologies News Key Categories

12. Data verification Finding and eliminating duplicate data Recognizing ambiguous data, e.g. finding correct person instances for names like John, Victoria Using a lexicon component to record the mappings between strings and instance names found in the previous processes Using contextual information to decide

13. NumberPeopleOrganizationsProjectsResearch Area Total Manual data93752523216 ESpotter finds77581713165 ESpotter Recall-rate0.8270.7730.680.5650.763 Initial Evaluation PeopleOrganizatio ns ProjectsResearch AreaTotal Total (discovered)84971915215 Wrong4181225 Precision rate0.95230.710.9470.860.883 Recall Precision

14. KMi Semantic Web

16. So What? At a basic level, the architecture works Automatic generation is key Services still limited Developing interesting services requires non trivial effort Brittleness is a problem You rapidly reach the boundaries of the knowledge held in KMi resources and performance decreases Badly needs integration with other similar resources No API. Data available only as sources

17. What should happen next Integration with other similar activities Hence this workshop…. Ability to bring in knowledge expressed in other ontologies Need for standardised APIs/knowledge servers Develop mechanisms for semantic annotation by side-effect Improve text mining technology to improve both the quantity and the quality of the knowledge Develop more value-adding services

18. Services defined for a particular Class in a particular Ontology are available to any system who asks for them Intg. with Sem Web Services