Semantic Technology and Ontology: Down to Business

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  • 1.5 m
  • Vennish Diagram
  • 1mOUR FOCUS: Real World MeaningContext: river bank, savings bank, banking an aircraft
  • 1.5mWeb of Data: LOD CrowdW3C Witness protection: Clay ShirkyRevolution: Mark Greaves
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  • 1-2 minQuery/explore across related topicse.g. news story about growing your own food e.g. navigate from TV program site to sites about the actorsToo costly to re-purpose content for new sitesLabor intensiveData feeds to developers disconnected
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  • 1 min
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  • Animation, Represent issues with icons, e.g. reliabilty is thumbs up, agility is a runner, self-describing data is number or word with graph on sleeve…Issues pop up then are connected by causal paths with links meaning ‘facilitates’ or ‘inhibits’ E.g. Invisible semantics inhibits understandabtility which inhibits ease of maintenance which inhibits flexibilty and agility. Self-describing data facilitates understandabilty. Easier to reuse facilitates fast evolution and agility. Automated reasoning facilitates consistency and reliability.Root of most problems are:Invisible Semantics Data SilosInformation and Complexity OverloadThese all have ripple effects on:Maintenance costs, Reuse, understandabiltiy, speed of evolution, agility, costs, reliability, consistency, Interoperability / Integration ‘We add three things that collectively address most of these problems1. self describing data – adding meaning 2. Easier data connections – Linked Data (overcome silos)3. Automated reasoning – to address complexity, reliability and add new capabilities
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  • Semantic Technology and Ontology: Down to Business

    1. 1. Copyright © 2010 Michael Uschold. All rights reserved.<br />Semantic Web: Down to Business<br />Michael Uschold, PhD<br />.<br />The majority of this talk is taken from “Semantic Web: Down to Business”, <br />presented Monday November 15, 2010<br />Taxonomy Boot Camp– Washington DC<br />1<br />
    2. 2. Page: 2<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Objectives <br />I will understand:<br />What is the “Semantic Web”<br />Real world applications of Semantic Web Technology<br />Where the value comes from<br />What I might want to do in my organization.<br />That the Semantic Web future is bright<br />
    3. 3. Page 3<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Outline<br />Introducing Semantic Web Technology <br />Practical Applications <br />The Future is Bright<br />What can I do?<br />
    4. 4. What is “Semantic Technology”?<br />Fundamental properties:<br />Data wears its meaning on its sleeve – metadata<br />Meaningful connections between data<br />Computer draws conclusions<br />Benefits: <br />AGILITY: faster, cheaper, flexible and adaptable<br />INTEGRATED: data connections, integrated applications<br />INTELLIGENT APPLICATIONS: new things are possible<br />4<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />I am what I am<br />
    5. 5. Artificial Intelligence and Semantic Technology <br />URI<br />XML<br />Artificial Intelligence<br />Unicode<br />Vision<br />Semantic Technology <br /> Knowledge <br />Representation &Reasoning<br />Semantic Web <br />Robotics<br />Creativity<br />OWL<br />RDF <br />SPARQL<br />Triple Stores<br />Planning<br />Machine Learning<br />Intelligent Agents<br />Natural Language Processing<br />Speech recognition<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Page: 5<br />
    6. 6. What do we mean by “semantics”?<br />The word “semantics” means: MEANING.Variations:<br />Everyday language: “its just semantics” quibbling over words<br />Natural language processing: syntax, semantics, pragmatics<br />Logic: guaranteed to draw correct conclusions<br />Data: meaning in the real world<br />Meaning is all about context & relationships<br />6<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />pilot<br />account<br />Bank<br />Bank<br />deposit<br />turn<br />savings<br />aircraft<br /><ul><li>Meaning for computers, not just people</li></li></ul><li>Page: 7<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Data Wearing Meaning on its Sleeve<br />Simple Task:<br />Find documents about mechanical devices.<br />The purpose of this review is to remind operators of the<br />existence of the Operations Manual Bulletin 80-1, which provides<br />information regarding flight operations with low fuel quantities,<br />and to provide supplementary information regarding main tank<br />boost pump low pressure indications.<br />747 FUEL PUMP LOW PRESSURE INDICATIONS <br />When operating 747 airplanes with low fuel quantities for short<br />
    7. 7. Page: 8<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />What the heck<br />is a Fuel Pump?<br />Semantic Annotation<br /><concept id=fuel-pump>FUEL PUMP</concept><br />fuel-pump<br /> a owl:class; rdfs:subClassOf SHR: pump<br />Meaningful Connection and Automated Reasoning <br />Shared Hydraulics Repository (SHR)<br />Pump<br />a owl:Class ;<br /> rdfs:comment "A mechanical device for raising, compressing, or transferring fluids.“; ; rdfs:subClassOf MechanicalDevice;rdfs:subClassOf<br /> [ a owl:Restriction ;<br />owl:hasValue Piston ;<br />owl:onProperty hasPart<br /> ] .<br />Hey, I know about, SHR, so now I know <br />something about <br />Fuel Pump.<br />The purpose of this review is to remind operators of the<br />existence of the Operations Manual Bulletin 80-1, which provides<br />and to provide supplementary information regarding main tank<br />boost pump low pressure indications.747 <concept id=fuel-pump>FUEL PUMP </concept> LOW PRESSURE INDICATIONS <br />When operating 747 airplanes with low fuel quantities for short<br />
    8. 8. Page: 9<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Computers Drawing New Conclusions<br />Deriving new information from existing information.<br />BENEFITS & USES:<br />Question Answering <br />Information Integration<br />Filtering<br />Reduced need to build custom processing engines<br />Guarantees of correctness – consistency checking <br />Compact representation, e.g. transitivity<br />Easier to understand and maintain<br />
    9. 9. Page: 10<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />What goes on the sleeve? <br />Comes from a “Semantic Model” for some subject area:<br />A way to capture meaning<br />Agreed terms, definitions and relationships<br />I am what I am<br />Pumping<br />Mechanical Device<br />done-by<br />Engine<br />Pump<br />Hydraulic System<br />has-part<br />supplies-fuel-to<br />Jet Engine<br />Hydraulic Pump<br />Fuel Pump<br />part-of<br />connected-to<br />Aircraft Engine Driven Pump<br />Fuel System<br />Fuel Filter<br />Page: 10<br />
    10. 10. Page: 11<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Different ways to capture meaning...<br />Examples: <br />Data dictionary, Glossary, Controlled Vocabulary<br />Thesaurus <br />Taxonomy <br />Ontology<br />Many others<br />
    11. 11. Page: 12<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Example: Controlled Vocabulary / Glossary<br />Pump: “A mechanical device for raising, compressing, or transferring fluids”<br />Engine: “a machine that turns energy into mechanical motion”<br />Mechanical Device: “a physical device with parts that move relative to each other”<br />
    12. 12. Page: 13<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Example: Taxonomy <br />= Generalization<br />Mechanical Device<br />Engine<br />Pump<br />Jet Engine<br />Hydraulic Pump<br />Fuel Pump<br />Aircraft Engine Driven Pump<br />
    13. 13. Page: 14<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Example: Thesaurus <br />= Broader Term<br />= Associated Term<br />+ Synonym & Homonym<br />Pumping<br />Mechanical Device<br />Engine<br />Pump<br />Hydraulic System<br />Jet Engine<br />Hydraulic Pump<br />Fuel Pump<br />Aircraft Engine Driven Pump<br />Fuel System<br />Fuel Filter<br />
    14. 14. Page: 15<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Ontology: Strict Taxonomy + Formal Relationships<br />= Generalization <br />= Other Relationships <br /><ul><li>Taxonomy with multiple link types, each with precise meaning, is usually called an “ontology”.</li></ul>Pumping<br />Mechanical Device<br />done-by<br />Engine<br />Pump<br />Hydraulic System<br />has-part<br />supplies-fuel-to<br />Jet Engine<br />Hydraulic Pump<br />Fuel Pump<br />part-of<br />connected-to<br />Aircraft Engine Driven Pump<br />Fuel System<br />Fuel Filter<br />
    15. 15. Page 16<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Approaches for Capturing Meaning <br />
    16. 16. So What IS the Semantic Web?<br />It depends who you ask!<br />A Web of data<br />A set of W3C standards<br />A technology base to be used on or off the Web<br />An upgrade to the existing Web<br />Witness protection plan for AI<br />A new application of AI:An intelligent machine-readable Web of knowledge!<br />A revolution in the way we think of data, crowds, & schema<br />17<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />
    17. 17. What the Semantic Web ISN’T<br />A silver bullet<br />A software package<br />Limited to being on the Web<br />A replacement for the existing Web<br />A mere figment of researcher’s imaginations<br />18<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />
    18. 18. Page 19<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Outline<br />Introducing Semantic Technology<br />Practical Applications<br />The Future is Bright<br />What can I do?<br />
    19. 19. Page: 20<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Out of the Research Labs: Deployed Systems<br />Growing number of Real Success Stories<br />Many are hidden:<br />Behind Corporate Firewalls – for competitive advantage<br />Classified government projects – for national security <br />Increasingly, they are becoming known.<br />
    20. 20. Example: British Broadcasting Corporation<br />Situation(in 2007)<br />Many handcrafted individual micro sitese.g. news, food, gardening<br />All data and content disconnected across sites<br />Hard to Do:<br />Query/explore across related topics<br />Find everything on a given topic<br />Re-purpose content for new sites<br />Leverage evolving data from external sites<br />21<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />
    21. 21. BBC: Connecting Multiple Data Sets<br />Latest Tracks<br />John Denver<br />Wikipedia<br /> FlickR?<br />Audio Previews<br />
    22. 22. BBC: Connecting Multiple Data Sets<br />IMDB<br />MySpace<br />MusicBrainz<br />Last.fm<br />Played by<br />Played on<br /> Reviews<br />
    23. 23. External Data Sets: “Linked Data Cloud”<br />
    24. 24. Solution: Web as Content Management System<br />Benefits: <br />Usability: Have sites on things that people care about<br />User Experience: Visualize resources in new ways<br />User Journeys: animal… program clip, related habit<br />Reuse Data: <br />One page per thing<br />Leverage external linked open data<br />Others can re-purpose BBC data to create new sites<br />Linkable and discoverable by humans and computers <br />SEO: Highly optimized for search engines <br />25<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />
    25. 25. Example BBC: Why did it Work?<br />26<br />Copyright © 2010 Michael Uschold. All rights reserved.<br /> Music<br /> Radio<br />TV<br />Why Did It Work? <br />Flexibility: DB-backed Web applications brittle not able to support changing environment.<br />Meaning of Data is clear<br />Connectivity: linking across data silos including <br />external data.<br />
    26. 26. Example: Manufacturing Quality Assurance (1/4)<br />Defective Widgets:<br />1 in a 1000 widgets coming of the line are defective<br />All have same defect<br />Challenge: <br />Enormously complex manufacturing process<br />Countless possible pathways<br />Very time consuming to track down, may not succeed<br />27<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />
    27. 27. Example: Manufacturing Quality Assurance (2/4)<br />SOLUTION: <br />Build models for various aspects of business<br />Each machine, components and attributes <br />Manufacturing process pathways<br />Products <br />Capture data during manufacturing process;based on the models<br />Query the system:<br />What is common among all defective widgets?<br />Answer: 99% of defective widgets came off one particular line<br />28<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />
    28. 28. Example: Manufacturing Quality Assurance (3/4)<br />OUTCOME: <br />System uses data & knowledge to draw conclusionse.g. identify machines as source of problem<br />Go look at machines, notice defective part, replace it.<br />Generate a report as well<br />Used to take a week, now takes 10 minutes.<br />Customer: “We love ontologies.” Continued investment.<br />29<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />
    29. 29. Why Did It Work? <br />Flexibility: Traditional DB applications brittle, not able to support changing environment.<br />Connectivity: Semantic models are basis for linking across data silos.<br />Drawing conclusions: In complex environment, reduce information overload.<br />30<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Example: Manufacturing Quality Assurance (4/4)<br />
    30. 30. EXAMPLE: KukaXprt <br />Kuka: No. 3 largest Robot Manufacturer in World … and growing fast!<br />Need: disseminate knowledge about robot handling & repair<br />End User: service engineers sent out for repairs<br />Goal: <br />collect knowledge of the experienced service engineers <br />support new service engineers <br />
    31. 31. Background<br /><ul><li>65% of all customer in the manufacturing industry change their suppliers because there are not satisfied with the service
    32. 32. Service engineers spend a lot of time with known problems</li></ul>Goal<br /><ul><li>Capturing and usage of engineers and experts know-how
    33. 33. Decision support for choosing the right solution
    34. 34. Increase customer satisfaction</li></ul>Implementation <br /><ul><li>Semantic Customer Service Support</li></ul>Customer Service Support for Kuka Roboter<br />
    35. 35. Customer Satisfaction and Competitiveness<br />Value proposition & Results <br />Reduce costs:<br /><ul><li>No more trial and error
    36. 36. Reduce ‚Time To Fix‘ and increase ‚First Time Fix‘
    37. 37. Reduce ‚Spare Part Overtake‘</li></ul>Improve Quality<br /><ul><li>Guided and quality assured problem solving</li></ul>Motivation of Service Engineers:<br /><ul><li>Easier handling compared to paper
    38. 38. Less work</li></ul>Alwin Berninger:<br />Director Customer Support<br />KUKA Roboter GmbH<br />“The project was completed successfully, due to the close collaboration with ontoprise and due to highly reliable and high quality of work from ontoprise” <br /><ul><li>Find the right solutions faster
    39. 39. More robots working more of the time
    40. 40. Increased customer satisfaction</li></li></ul><li>How do ontologies and semantics help?<br />While some of these tasks can sometimes also be accomplishedby conventional technology, ontologies are the superior technology when it comes to combining these tasks.<br /> They [ontologies] are reuasable knowledge modules that capture the domain logic as seperate, descriptive assets. They are very flexible and extendable. They serve as a content backbone to which all the tasks can refer to. <br /> When using conventional technologies in the Kuka case it would be much harder to deal with changes in the robots models and to extend the background knowledge, since a procedural system would probably require a reimplementation. It would also be much harder to combine the integration, search and guiding process, since the ontology as central backbone would be missing. Wolf Winkler, Ontoprise]<br />“<br />”<br />
    41. 41. Why does it work? IT Challenges and Root Causes<br />35<br />Hinders<br />Helps<br />Cheaper<br />Evolution/Agility/Flexibility<br />Maintenance <br />Interoperability / Integration <br />Reuse<br />Consistency/Reliabilty<br />Understandability<br />What does it mean?<br />(ambiguity)<br />Data Silos<br />Info. Overload & Complexity<br />
    42. 42. Page 36<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Outline<br />Introducing Semantic Technology<br />Practical Applications <br />The Future is Bright<br />What can I do?<br />
    43. 43. Page: 37<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Recent Developments<br />This Year: <br />Sept 20: WebMediaBrands buys SemTech Conference & Site from Semantic Universe<br />July 16: Google buys Metaweb (Freebase)<br />April 28: Apple purchases SIRI<br />April 21: Facebook announces release of social graph<br />2007-2009<br />Sept 2009: W3Cguidelines for publishing open data<br />June 2009: NY Times releases 100 year old thesaurus<br />May 2009: Whitehouse unveils Open Gov’t Initiative<br />May 2009: Google announces Rich Snippets <br />July 2008: Microsoft buys Powerset<br />2007-2009: Linked Data Cloud explodes on the scene<br />
    44. 44. Recent Trends<br />Semantic Technology Conf. grows through recession<br />Semantic Technology Companies / Consultancies<br />2005: a few dozen<br />2010: several hundred<br />Bigger companies buying smaller ones<br />Patent Applications: <br />90s: a handful per year<br />Pre-recession: triple digits<br />Semantic Web Meetups: <br />100% annual growth for 3 yrs<br />38<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />
    45. 45. Premiere Business Conference for Semantic Technology <br />Speakers from 155 different companies for 2010:<br />39<br />Copyright © 2010 Michael Uschold. All rights reserved.<br /><ul><li>Siemens
    46. 46. Best Buy
    47. 47. Google
    48. 48. Yahoo
    49. 49. Nokia
    50. 50. Wells Fargo
    51. 51. Oracle
    52. 52. Microsoft
    53. 53. Boeing
    54. 54. Merck
    55. 55. Pfizer
    56. 56. SAP</li></li></ul><li>Widespread Interest in Semantic Technologies <br />Health / Pharmaceutical Life Sciences<br />Enterprisee.g. Salesforce.com<br />Advertising and Marketing <br />Retaile.g. Best Buy, Nokia<br />Content PublishingDigital Libraries<br />Finance<br />Military Intelligence<br />Open Government<br />Energy Management<br />40<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />
    57. 57. Page 41<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Outline<br />Introducing Semantic Technology<br />Practical Applications <br />The Future is Bright<br />What can I do?<br />
    58. 58. What you might want to do…<br />Create an Agile Semantic Enterprise <br />Invest in training in semantic technology.<br />Develop potential use cases for semantic applications in your organization. Charter a pilot in coming year.<br />Begin thinking about your data / information architecture in terms of semantic models and leverage existing models (“ontologies”) in your industry.<br /> Ref: Cutter IT Journal. Vol. 22, No. 9 September 2009<br />42<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />
    59. 59. Page: 43<br />Copyright © 2010 Michael Uschold. All rights reserved.<br />Building Your Own Semantic Application <br />Identify a value propositionDriven by Business, not IT department!<br />What is the role of the semantics technology?<br />How will semantics help?<br />Why is it better than alternative approaches?<br />Cost / benefit analysis <br />Build proof of concept first...<br />... Then put into production.<br />

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