1. Semantic Spaces Professor Nigel Shadbolt Director of AKT School of Electronics and Computer Science University of Southampton

2. Structure of the Talk Introduction Semantic Spaces: The vision and the reality Ingredients for Semantic Spaces –Ontologies –Heterogeneous Information Sources –Navigation and visualisation of the space –Knowledge Processing Services –Socio-technical Challenges Interleave with two primary examples from AKT –CS ATKive Space http://triplestore.aktors.org/demo/AKTiveSpace/ –MIAKT www.aktors.org/miakt

3. Advanced Knowledge Technologies IRC AKT started Sept 00, 6 years, £8.8 Meg, EPSRC www.aktors.org Around 65 investigators and research staff

4. Dramatis Personae DepartmentsPIsCIs Computing Sciences, Aberdeen Derek SleemanPeter Gray Alun Preece Informatics, EdinburghAustin Tate Dave Robertson KMI, OUEnrico MottaSimon Buckingham-Shum John Domingue Computer ScienceYorick WilksFabio Ciravegnia Hamish Cunningham ECS, SouthamptonNigel Shadbolt Wendy Hall Leslie Carr Dave De Roure Hugh Glaser Kieron O’Hara

5. Semantic Spaces: The Vision

6. Structured Spaces Linkage of heterogeneous information –web content –databases –meta-data repository –multimedia Via ontologies as information mediation structures Using Semantic Web languages Oncogene(MYC): Found_In_Organism(Human). Gene_Has_Function(Transcriptional_Regulation). Gene_Has_Function(Gene_Transcription). In_Chromosomal_Location(8q24). Gene_Associated_With_Disease(Burkitts_Lymphoma). NCI Cancer Ontology (OWL) bcr-2-1-059 BioMedCentral Metadata (XML) Web data set (XHTML) Vocabulary (RDFS)

7. Services on the Space Hendler 03- Science

8. So what have we got? A very particular KRL for the web Very low take up of structured meta-data beyond XML What RDF exists is largely FOAF A variety of demonstrators on the small to medium scale Few deployed examples A lot of Good Old Fashioned Artificial Intelligence (GOFAI) proposals in the wings But this could be our big chance….

9. Semantic Spaces: Ontologies

10. Perspectives on ontologies Source, Concepts for Automating Systems Integration, E. Barkmeyer, A. Feeney, P. Denno, D. Flater, D. Libes, M. Steves, E. Wallace. NISTIR 6928, NIST Feb., 2003 The semantic view: An ontology is the context needed to understand a specification, model, or other communication in the way that was intended. The specification / reference view: "An ontology is an explicit specification of a conceptualization." and "Commitment to a common ontology is a guarantee of consistency [in terminology]." Simple taxonomies and thesauri are included in this definition as degenerate cases. The modeling view: An ontology is a metamodel. The automation view: An ontology is, or is captured in, a knowledge base designed to support automatic reasoning.

11. Ontologies offer…. Communication –Normative models –Networks of relationships –Consistent and unambiguous –Integrate multiple perspectives Inter-operability and Integration: Sharing & Reuse –Inter-lingua –Specifications –Reliability Control –Controlled vocabularies –Accurate data collection or retrieval –Classification –Finding, sharing, discovering, navigation, indexing

12. Medicine: The UMLS® Extensive Medical Nomenclature Project Integrative –SnoMed Translation work into OWL Being widely adoped High level Governmental support

13. Genetics: Gene Ontology One of the earliest examples of the benefits of ontologies Integration and interoperability were big wins Specific tool support Considerable resources invested and continuing in maintenance Translation into DLs Spawned more generic biological ontology efforts

14. Manufacturing: Aerospace Considerable work on ontologies for products and components Used in all stages of the life cycle, from design to in service maintenance Need for multiple perspectives e.g –Whole engine –Heat transfer –Cost model –Manufacturing –Assembling/Maintenance

15. Military: Coalition Operations Some of the original motivation behind DAML work Lots of activity to build ontologies in a range of contexts Particularly important in coalition operations Central requirement for the concept of Network Enabled Capability

16. Computer Science: The AKT Ontology Designed as a learning case for AKT Adopted for our own Semantic Web experiments including CS AKTive Uses a number of Upper Ontology Fragments Reusable in many University and Research Contexts

17. MIAKT: Multi-disciplinary Assessment Multiple stakeholders Multiple viewpoints and vocabularies –Breast imaging – X-ray, ultrasound, MRI –Clinical examination –Microscopy – cells and tissues (also, hormone receptors) Local dialects in use Variation between countries due to factors such as insurance claims!

18. Ontologies: Observations In any domain –Usually highly implicit –Poorly documented –Likely to be ambiguous, vague, inconsistent When modelling –Interaction Problem: tasks influence ontologies –Integration Problem: integrating multiple ontologies –Modularity Problem: how to modularise and what grain size? Maintenance –Ongoing maintenance overhead –Ontologies evolve and change –Design rationale is important Upside –They do facilitate interoperability –They do enhance reuse –They are becoming part of the infrastructure

19. The Crucial Role Standards Play HTMLXML + Name Space + XML Schema Topic MapsSMIL RDF(S) XOL OWL RDF UnicodeURI

20. Semantic Spaces: Heterogeneous Information Sources

21. What might Heterogeneous Information Sources mean? Provenance –Could be legacy –Not necessarily under direct control –Variable validity Form –More or less structured –Different syntactic and semantic formats –Multimedia –Distributed in space or time Function –Collected for different reasons

22. MIAKT DEMO

23. Clinical examination –Notes Imaging –X-ray, –Ultrasound –MRI Microscopy –Histopathology Treatment –Protocol Records –Re-assessment Medical Records –Case sets –Individual patient records Published background –Epidemiology –Medical Abstracts

24. AKTive Spaces Content harvested and published from multiple Heterogeneous Sources Higher Education directories 2001 RAE submissions UK EPSRC project database (all grants awarded by EPSRC in the past decade) Detailed data on personnel, projects and publications harvested for: –all AKT partners –all 5 or 5* CS departments in the UK –Automatic NL mining: Armadillo Additional resources –All the world's countries (from ISO3166-1) –All UK administrative areas (from ISO3166-2) –All UK settlements listed in the UN LOCODE service –All the world's airports (from the IATA) –(and they're all integrated via the AKT reference ontology)

25. Semantic Spaces: Navigation and Visualisation

26. Aspects to navigating or visualising a semantic space Semantic Interfaces –Ontology as a navigable structure –Semantic encoding visualisations Scope –Local to global –Domain specific or generic Function –Reader to author –Individual to collaborative

27. Navigation and visualisation via graphical characterisation of ontology Ontological relations are also the essential relations that are used to navigate the information space Natural Language Generation is used to provide a summary of content held in the image

28. W3photo and AKTive photo An AKTive Space for photo annotation The annotation is direct from the ontology The navigation is also based on the ontology Re-ordering columns (classes) exposes different parts of the information space

29. CS AKTive Space An AKTive Space for CS research The navigation is also based on the AKT ontology Re-ordering columns (classes) exposes different parts of the information space Complex RDQL dispatched behind the direct manipulation interface Strong geographical overaly

30. Semantic Spaces: Knowledge Processing Services

31. What constitutes a semantic service? Have semantic characterisation –What is the goal or task achieving effect? –What are its “knowledge level” preconditions or inputs Compositionality –Grain size –Internal and external aspects Discoverable or locatable –Accessible –Maintained

32. MIAKT: Overall Framework and Current Services

33. CS AKTive Space Services Triple Store (3store) and associated browser navigation RDQL interface to 3store Harvesting and scraping M-space visualisation Community of Practice Armadillo – publication harvesting

34. The CS AKTive Space: Semantic Web Challenge Winner 2003 24/7 update of content Content continually harvested and acquired against community agreed ontology Easy access to information gestalts - who, what, where Hot spots –Institutions –Individuals –Topics Impact of research –citation services etc –funding levels –Changes and deltas Dynamic Communities of Practice…

35. CS AKTive Space DEMO

36. Semantic Spaces: Socio- Technical Context

37. Semantic Spaces: A Challenge? “Is this rocket science? Well, not really …We are not inventing relational models for data, or query systems or rule- based systems. We are just webizing them. We are just allowing them to work together in a decentralized system - without a human having to custom handcraft every connection.” Tim Berners-Lee, Business Case for the Semantic Web, http://www.w3.org/DesignIssue s/Business

38. Technical Challenges in Semantic Space Annotation Content capture/harvesting Ontology mapping and alignment Referential Integrity Reasoning Services including incorporation of statistical and probabilistic methods Semantic service composition Provenance and Trust Multimedia content Semantic HCI

39. Social Challenges of Semantic Spaces Social Issues –How do you get communities to participate? –Mandate and require –The need to share information e.g. e-Science –Become social and viral e.g. early days of web and FOAF Regulatory –Fidelity of content is on the high side but even so…provenance and quality services –Data Protection and information assurance