In search of lost knowledge: joining the dots with Linked Data


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These slides are from my seminar to the University of Reading Department of Meteorology, November 2013. They contain a (hopefully not very technical) introduction to the concepts of Linked Data and how we are applying them in the CHARMe project ( In CHARMe we are using Open Annotation to connect users of climate data with community-generated "commentary information" that helps them to understand a dataset's strengths and weaknesses.

The slide notes contain some helpful context, so you might like to download the PPT file!

The slides are licensed as "Creative Commons Attribution 3.0", meaning that you can do what you like with these slides provided that you credit the University of Reading for their creation. See

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  • Here’s a simplified representation of the things we are interested in in Earth Observation
  • And here’s a more complex version. The instrument produces “Level 0” data, which are then processed using a series of algorithms to produce something that the user community finds easier to use.
    Documents may of course be written about any of these steps. All these documents will be written by different people at different times, and possibly about different versions of the same thing
  • Now let’s say you only know about a few of these things – a few publications, the algorithms you know already and the latest versions of a few datasets. Where do you go from here?
  • This person is trying to make a good decision based on climate data from satellites and from computer simulations. She also needs to know about other factors such as population growth and changes in urbanization. It’s a pretty hard job to bring together information from all these communities and correlate them. Everyone will use different standards, formats and terminology
  • If you are in a data-rich field that relies a lot on statistics, you may well be in this situation where the false positives outweigh the true ones. (95% test accuracy does not mean that there is a 95% chance that your hypothesis is true!)
    Obviously, you can play with the numbers here, but in general this will be significant if you’re in the case where most hypotheses are false.
    This may contribute to findings that results in the peer-reviewed literature can’t be reproduced – not necessarily due to poor experiments or tests.
  • Sites such as FigShare allow publication of stuff that would not make it into the peer-reviewed literature, but that are still useful.
  • Who here uses NASA instead of ESA because the data are easier to get hold of? NCEP instead of Met Office?
  • Just a reminder of what our simplified situation in EO looks like
  • This is how the Web represents our case: just a set of documents with simple links. The Web records no information about what the documents represent, or why they are linked.
  • Lots of links here, which is good! However, you need context (and natural language understanding) to determine what the links mean – are they affiliations, projects Richard works on, links to “institutional” (not personal) stuff, or what? A computer can’t reason based on this information (although link density gives some indication of relevance and popularity).
  • Here’s part of the citation list from one of my recent papers. Some of these are papers, some are standards, some are simple URLs. There is no information about why I’m citing them, unless you trawl the text and divine my meaning.
  • These are some of the problems of using documents-about-things to talk about stuff
  • These identifiers may look like web addresses (Uniform Resource Locators) but in fact are identifiers – just strings. You don’t expect to download me when you put my identifier in your web browser but you might get a representation of me (e.g. my home page)
  • The bottom one is a paper citing a dataset as a data source. Note that we can say *why* we’re citing the data
  • Your homework is to apply the same technique to the joke about the Dalai Lama going to a hot-dog stall and saying “make me one with everything”
  • The point about being part of the web is important, but hard to grasp. Your data can be found by semantically-aware web crawlers, which might be able to do something interesting with it, rather than just finding a document that requires a human to read.
  • So the Web of Data can more accurately record our situation, and can describe the nature of the links between things.
  • Lots of data, particularly public sector data, is being released as Open Linked Data
    Met Office
    Ordnance Survey
    Office of National Statistics
    People need to publish not only their data, but the terms that they use and the relationships between them
  • A close-up of some of the geospatial part. Note the Met Office.
  • So what are we doing with Linked Data in the climate field? Here is a European project, about half way through,
  • Simple “value chain” for climate data observed from space.
  • See next slide for explanation of this
  • This is basically the same as the previous slide in words instead of pictures! The last paragraph is crucial – we’re looking at stuff the data provider doesn’t already know.
    We’re not trying to turn the whole of climate data into Linked Data, but focusing on user-provided annotations
  • Important to note that decision-makers will probably not use CHARMe directly
  • So this person might be able to use CHARMe to find interesting information from all these communities, and find out what the experts in those communities think about the data. It will be easier to discover the common pitfalls.
  • CHARMe is often compared with Amazon, Tripadvisor etc, but the user base will be smaller and more specialist, therefore it is important for the information to be accurate. Can’t rely on the wisdom of crowds!
  • This is the most basic use case for CHARMe. In the next couple of slides we’ll look at more advanced uses. The fact that the annotations (i.e. the commentary information) will be machine-readable means that it’s possible to build all kinds of applications.
  • Here’s an “advanced application” for using CHARMe information, which will be correlated with climate reanalyses and external events.
  • Here’s another project that is just starting, which will use Linked Open Data to create new services and products. Many of the participants are small and medium-sized enterprises who will base business ideas on Open Data.
  • In search of lost knowledge: joining the dots with Linked Data

    1. 1. In search of lost knowledge: joining the dots with Linked Data Jon Blower Department of Meteorology Reading e-Science Centre National Centre for Earth Observation University of Reading With thanks to all CHARMe project partners!
    2. 2. instrument algorithm platform dataset scientists publication
    3. 3. How do you fill in the blanks? • Hope the documentation contains pointers • … to the right versions of things • … and using consistent terminology • In restricted communities, we can gather all information into a central location and harmonize it • OR we hope that a Google search throws up the right results
    4. 4. Problems 1. Crossing communities is very hard
    5. 5.
    6. 6. Problems 1. Crossing communities is very hard 2. Lots of useful stuff isn’t formally documented
    7. 7. Lots of useful negative results, which aren’t published Nearly 1/3 of positive results are false 1000 hypotheses 100 are true 900 are false Test accuracy 95% 95 true positives 5 false negatives 45 false positives 855 true negatives
    8. 8. Problems 1. Crossing communities is very hard 2. Lots of useful stuff isn’t formally documented 3. Unknown unknowns…
    9. 9. “There is a spurious decline in low-cloud fraction in the ISCCP cloud database due to the viewing angle. It’s in the literature, but you might not find it if you’re not specifically looking for it. For example if you’re using the data for model validation you may not spot the problem.” (Claire Barber, paraphrased)
    10. 10. Consequences • We use the data that are most easily available, not necessarily the best • Constant re-discovery of the same issues • Very hard to share information outside communities
    11. 11. The “Web of Data”
    12. 12. instrument algorithm platform dataset scientists publication
    13. 13. The Web is the “Web of Documents”
    14. 14. What is being cited? Why?
    15. 15. Fingers crossed! • We hope that everything has a document written about it, or we have nothing to cite • … and we hope someone writes a new document when “it” changes • We hope that we are citing the right document (i.e. the most authoritative etc)
    16. 16. “If … the Web made all the online documents look like one huge book, [the Sir Tim Berners-Lee Semantic Web] will make all the data in the world look like one huge database.” Photo by Susan Lesch, from Wikipedia
    17. 17. Why is the Semantic Web different? • Focuses on things, not documents-aboutthings • Links things together in a meaningful way • Information is readable by computers • Here’s how it works…
    18. 18. 1. Give things unique identifiers Must be globally unique and persistent might represent me might represent the MODIS instrument –(these are illustrative, not accurate!)
    19. 19. 2. Express relationships between things The key concept is the triple subject predicate object E.g. “MODIS is carried by the Terra satellite” all three things need to be unique identifiers (we don’t use plain English!)
    20. 20. Examples of realistic triples (identifiers are illustrative, not necessarily correct!)
    21. 21. Aside: how to use the Semantic Web to ruin jokes “A hundred kilopascals go into a bar” “100000”^^unit:Pascal owl:sameAs “1”^^unit:Bar (Using OWL and QUDT ontologies)
    22. 22. 3. Publish your triples • Can be as simple as putting a document in the right format on your website – (triples can be expressed in many formats) • … or as complex as publishing a multi-billiontriple database – E.g. Ordnance Survey • Either way, your data become part of the Web, not just on the web
    23. 23. 4. Profit! • Everyone can publish their own “view of the world” – Don’t need a single data structure “to rule them all” • Use common identifiers and common vocabularies to link between communities • Different vocabularies can be mapped to each other – E.g. map CF standard names to GRIB codes • Gives a means to record provenance of information – “Direct line of sight from decisions to data” - NASA • Reasoning engines can traverse the graph of links and discover new information
    24. 24. Example: SemantEco Wildlife observations Ecosystem impacts Administrative areas Pollution data and policies “Find me all the sites where chloride pollution exceeds the local policy limits” “Show me the species over time in this region”
    25. 25. So what’s “Linked Data” then? • The Web is the “web of documents” • The Semantic Web is the “web of data” • “Linked Data” is really a set of principles for using the Semantic Web – • (Many people use “Linked Data” and “Semantic Web” somewhat interchangeably)
    26. 26. carries produced authored produced describes / uses / queries … The “Web of Data”
    27. 27. Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.
    28. 28. CHARMe: Sharing climate knowledge through Linked Data (Jan 2013 – Dec 2014)
    29. 29. From observations to decisions Observations Satellites (obs. sometimes used for model initialization) Climate Record Creation and Curation Climate Data Records Reports Analysis and applications Decision making Decisions, Policies Model results Climate Modelling (Adapted from Dowell et al., 2013), “Strategy Towards an Architecture for Climate Monitoring from Space”)
    30. 30. Where can users go for help? • Scientific literature – Huge, verbose and inaccessible to some communities – Not well linked to source data • Technical reports and conference proceedings – Hard to find, scattered or inaccessible • Data centres – increasingly strong at providing some important metadata, but don’t usually include community feedback – Not all countries and communities have data centres! • Websites and blogs – From CEOS Handbook to a scientist’s blog – Increasingly useful, but scattered
    31. 31. How can climate data users decide whether a dataset is fit for their purpose? (N.B. We consider that “data quality” and “fitness for purpose” are the same thing) Not specific to climate data!
    32. 32. “Commentary metadata” 37
    33. 33. Examples of commentary metadata • Post-fact annotations, e.g. citations, ad-hoc comments and notes; • Results of assessments, e.g. validation campaigns, intercomparisons with models or other observations, reanalysis; • Provenance, e.g. dependencies on other datasets, processing algorithms and chain, data source; • Properties of data distribution, e.g. data policy and licensing, timeliness (is the data delivered in real time?), reliability; • External events that may affect the data, e.g. volcanic eruptions, ElNino index, satellite or instrument failure, operational changes to the orbit calculations. General rule: information originates from users or external entities, not original data providers – However, sometimes information is not available from the data provider!
    34. 34. How will this be done? • CHARMe will create connected repositories of commentary information Data provider website rd 33rdparty party system system – Stored as triples in “CHARMe nodes”, • Information can be read and entered through websites or Web Services • Using principles of Open Linked Data and the Semantic Web CHARMe node CHARMe node CHARMe node
    35. 35. Open Annotation • We are using Open Annotation for recording commentary – World Wide Web Consortium standard • Associates a body with a target • E.g. a publication could be the body, an EO dataset could be the target • Can record the motivation behind an annotation – Bookmarking, classifying, commenting, describing, editing, highlighting, questioning, replying… (lots more) – Covers a lot of CHARMe use cases! • An annotation can have multiple targets – A key requirement from users
    36. 36. Where does CHARMe fit in? Observations Satellites (obs. sometimes used for model initialization) Climate Record Creation and Curation Climate Modelling (e.g. CMIP5) Climate Data Records Reports Analysis Analysis and and applications applications Decision making Decisions, Policies Model results Supports analysts and scientists in production of information for decision-makers
    37. 37.
    38. 38. Challenges • Ensuring community adoption: – We are “injecting” CHARMe capabilities into existing websites used in the community – We will “seed” the CHARMe system with information to attract users (e.g. links between publications and datasets) • Ensuring quality of commentary metadata – Moderation is a strong user requirement – We will provide guides to creators of commentary – what makes a comment helpful?
    39. 39. What CHARMe will enable (some examples) Users: - “Find me all the documents that have been written about this dataset” - “… in both peer-reviewed journals and the grey literature” - “… and specifically about precipitation in Africa” - “… in both NEODC’s and Astrium’s archives” - “What factors might affect the quality of this dataset?” e.g. upstream datasets, external events - “What have other users already discovered about this dataset?” - “I want to find information related to the dataset I’m looking at” Data providers: - “Who is using my dataset and what are they saying about it?” - “Let me subscribe to new user comments and reply to them”
    40. 40. What CHARMe will not enable • “Give me the best dataset on sea surface temperature” – The “best” dataset depends on the application • CHARMe will not provide a new “quality stamp” for datasets – But will be able to link to such things if other people publish them, e.g. Bates maturity index, QA4EO certification • CHARMe will not provide access to actual data – (but will enable discovery of data) • Not planning to create (another) “one-stop shop” for information – We want the information to appear where users are already looking
    41. 41. Beware of 5-star ratings… (words of wisdom from TERRIBLE
    42. 42. How will you use CHARMe? • Search for datasets on existing data provider website • Use the “CHARMe button” to view or record commentary on a particular dataset
    43. 43. “Significant events” viewer • • • • Google Finance (above) matches stock prices with news events CHARMe tool will match climate reanalysis data with “significant events” – Algorithm changes, instrument failures, new data sources Will allow user annotations on the data and events Will be available on the Web
    44. 44. Fine-grained commentary • Will allow creation and discovery about specific parts of datasets • E.g. variables, geographic locations, time ranges • cf
    45. 45. Jon Blower Debbie Clifford Tristan Quaife Sam Williams • • Using Linked Data to combine EO and socioeconomic data in 8 application areas 16-partner EU FP7 project, just started! – Coordinated from Reading
    46. 46. Summary • Linked Data can join up information from all over the Web • Makes disparate data sources discoverable and processable • CHARMe is using Linked Data techniques to help users of climate data to connect with all the experience in the community – Techniques are not specific to climate data • MELODIES project will combine environmental data and socioeconomic data to develop real-world services using Linked Data
    47. 47. Thank you! @Jon_Blower @MelodiesProject
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