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Developing Semantic Web Sites: Results and Lessons Learnt

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. Motivation for KMi Sem Web. Key Objective

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Developing Semantic Web Sites: Results and Lessons Learnt

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  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. Story Relates-event Event People Project Organization Technology

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

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

  6. 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

  7. KMi Semantic Web Site Source Data Integration Layer Verification Layer Target Data Raw KB Information Extraction Engine (Espotter) Docs KB DBs XML mark-up Data Verification Engine DBs Mapping Engine Mapping Specs Domain ontology

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

  9. 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

  10. Initial Evaluation Recall Precision

  11. KMi Semantic Web

  12. 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

  13. 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

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

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