SUMMARY Southampton <ul><li>July-Oct, 2004, £10K </li></ul><ul><ul><li>Building a KB for joint access of data from EPSRC, ...
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Advanced Knowledge Technologies (AKT) -highlights 2006

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selection of web based semantic technologies prototypes used in the AKT programme.

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Advanced Knowledge Technologies (AKT) -highlights 2006

  1. 1. SUMMARY Southampton <ul><li>July-Oct, 2004, £10K </li></ul><ul><ul><li>Building a KB for joint access of data from EPSRC, MRC, and BBSRC </li></ul></ul><ul><ul><li>Visualisation and browsing tools </li></ul></ul><ul><li>Jan-April, 2005, £10k </li></ul><ul><ul><li>Mapping EPSRC to HESA </li></ul></ul><ul><ul><li>Publications: received 1000 CVs from LSI grant holders </li></ul></ul><ul><ul><li>Produce a set of statistics for LSI members based on number of collaborations with EPS and LS individuals per year </li></ul></ul>EPSRC-LSI <ul><li>ontology structure analysis and ranking (AKTiveRank) </li></ul><ul><li>discipline analysis (EPSRC Life Sciences Interface (LSI) Pilot studies) </li></ul><ul><li>semantic integration (CROSI project – jointly with HP Labs @Bristol) </li></ul>http://eprints.ecs.soton.ac.uk/10736/ <ul><li>ontology features’ classification (MIAK) </li></ul><ul><li>(mobile) semantic services (mSpace mobile WAI project) </li></ul>Acquisition Modelling Reuse Publishing Retrieval Maintenance AKTiveRank <ul><li>Ranking ontologies based on a number of measures : </li></ul><ul><ul><li>Class Match Measure </li></ul></ul><ul><ul><ul><li>Measures the degree of match between search terms and concept names </li></ul></ul></ul><ul><ul><li>Density Measure </li></ul></ul><ul><ul><ul><li>Measures how dense the representation of a concept is in the ontology </li></ul></ul></ul><ul><ul><li>Centrality Measure </li></ul></ul><ul><ul><ul><li>How centrally located a concept is wrt to its hierarchy </li></ul></ul></ul><ul><ul><li>Semantic Similarity Measure </li></ul></ul><ul><ul><ul><li>How close are the concepts we are searching for in the ontology </li></ul></ul></ul><ul><li>A 12 months joint project with HP Labs @Bristol </li></ul><ul><ul><li>Nov’05 – Nov’06, £96k, 1xRF (bh@soton) </li></ul></ul><ul><ul><li>AKTors involved: yk1, nrs (@soton) </li></ul></ul><ul><li>Theme : Semantic Integration (SI) </li></ul><ul><li>Aim : capture semantics, represent them in KR formats, & operationalise them to achieve the desired SI </li></ul><ul><li>Background : builds on existing AKT work and expertise on ontology mapping (2002-2004, yk1@soton, marco@edin) </li></ul><ul><li>1 st deliverable: a comprehensive survey of the current state-of-the-art in SI –- comparative in style, DB and AI solutions </li></ul><ul><li>Current work: design, develop, deploy a proof-of-concept SI system More on: http://www.aktors.org/crosi/ </li></ul><ul><li>CROSI is currently developing a modular architecture for semantic integration </li></ul><ul><ul><li>Based on a step-wise iterative process: </li></ul></ul><ul><ul><ul><li>generation of features from ontology models to be merged/aligned/mapped </li></ul></ul></ul><ul><ul><ul><li>selection of features to use for kicking-off the feature matchers </li></ul></ul></ul><ul><ul><ul><li>selection and invocation of suitable feature matchers </li></ul></ul></ul><ul><ul><ul><li>multi-strategy similarity aggregator which combines heterogeneous results </li></ul></ul></ul><ul><ul><ul><li>similarity evaluator and user feedback loop </li></ul></ul></ul><ul><li>CROSI project 6 th month deliverable </li></ul><ul><ul><li>Semantic Integration Technologies Survey ( http://eprints.ecs.soton.ac.uk/10842/) </li></ul></ul><ul><ul><li>44 state of the art systems, frameworks, tools, methods, algorithms reviewed </li></ul></ul><ul><ul><li>Reference classification scheme was distilled - the Semantic Intensity Spectrum </li></ul></ul><ul><ul><li>It could be used to </li></ul></ul><ul><ul><ul><li>inform requirements for building semantic integration systems </li></ul></ul></ul><ul><ul><ul><li>catalogue and classify semantic integration systems </li></ul></ul></ul><ul><ul><ul><li>better understand the semantic integration field </li></ul></ul></ul>CROSI - Capturing, Representing and Operationalising Semantic Integration <ul><ul><li>Exemplar features we extract/generate: </li></ul></ul><ul><ul><ul><li><owl:Class>, <owl:ObjectProperty>, <owl:DataTypeProperty>, <owl:Restriction>, <rdfs:subclassOf>, <owl:unionOf>, <owl:disjointWith>, <rdfs:comment>, <owl:intersectionOf>, <owl:sameAs>, <owl:equivalentClass>, and many more OWL constructs </li></ul></ul></ul><ul><ul><li>Exemplar feature matchers: Protégé PROMPT/DIFF/ANCHOR, GLUE, QOM, S-Match, IF-Map, INRIA Alignment API, etc . </li></ul></ul><ul><ul><li>Work in progress: Aggregator, Evaluator </li></ul></ul><ul><ul><li>Initial case studies: EON & I3CON experiments, UMBC time & AKTRef time </li></ul></ul><ul><ul><ul><li>myGrid & myGridReasoned ontology mapping scenarios </li></ul></ul></ul>http://www.aktors.org/crosi/ mSpace mobile WAI (where am I) Semantic Web content in context, live with domains on demand, and recommendation/trust services I’m at Somerset House for a meeting and have 45 mins before it starts: what historical sites can I visit - is there a coffee shop nearby that someone I trust thinks is good. Wasn’t I at this place last month? Heh, I want to see this new movie. Is there somewhere close by I can see it in time to get my train home tonight? Any sushi restaurants close by? How does one pick good sushi? What’s sashimi? New mSpace distributed architecture for easy publishing, distributed queries and new form factors: Taking mSpace generic so that predefined domains can be expanded on demand (History into Classical Music , into Contintal Cuisine mSpace mobile WAI http://mspace.fm/ MIAKT <ul><li>Preliminary ideas: use of classifiers (BayesNets, NeuralNets, SVMs) to: </li></ul><ul><ul><li>Classify features to a feature ontology </li></ul></ul><ul><ul><ul><li>Formalise concepts for describing media items (e.g. ‘round’, ‘spiky’, ‘orange’, ‘moving-left’, etc.) </li></ul></ul></ul><ul><ul><ul><li>Removes classifier dependence on feature type </li></ul></ul></ul><ul><ul><li>Classify feature ontology instances to domain ontology instances based on relations between features (perhaps encoded as ontology from which classifiers are built?) </li></ul></ul><ul><ul><ul><li>e.g. [spiky and round and (orange or yellow)] thing is a ‘cartoon-sun’. </li></ul></ul></ul><ul><ul><ul><li>e.g. [(f left-of o left-of r left-of d) inside (blue and oval)] thing is a ‘ford-logo’. </li></ul></ul></ul><ul><ul><ul><li>Generally: </li></ul></ul></ul><ul><ul><ul><ul><li>([feature|statement] relation [feature|statement]) is domain-concept . </li></ul></ul></ul></ul><ul><li>MIAKT completed at the end of March </li></ul><ul><li>27-page Journal paper nearing submission </li></ul><ul><li>Continued development of architecture, focussing on capturing knowledge from media </li></ul><ul><ul><li>Extensions to MIAKT Architecture therefore it is necessary to be domain and media independent </li></ul></ul>http://www.aktors.org/miakt/

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