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Semantic Web / Linked Data Technologies

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Semantic Web / Linked Data Technologies

  1. 1. Semantic Web Linked Data Technologies Mathieu d’Aquin (@mdaquin) Knowledge Media Institute, The Open University, UK
  2. 2. Semantic Web Linked Data Technologies Mathieu d’Aquin (@mdaquin) Knowledge Media Institute, The Open University, UK Research Fellow – Background in Artificial Intelligence, Knowledge Engineering, Reasoning Working on Semantic Web, Linked Data and Knowledge Technologies Especially applied to education and personal information management/Privacy Research Lab, ~75 people, many industrial and academic collaborations, Leader in semantic web, linked data,TEL, learning analytics, new media research Open and Distance Learning University, the biggest university in the UK in number of students (~250,000 per year), 13 regional centres, + national centres. Almost all teaching at distance.
  3. 3. The Semantic Web Using theWeb to publish, share and exploit information/knowledge From machines to machines Using graph-based data modeling, knowledge representation (ontologies) and reasoning
  4. 4. Linked Data As set of principles and technologies for a Web of Data – Putting the “raw” data online in a standard representation (RDF) – Make the data Web addressable (URIs) – Link to other Data http://lucero-project.info/lb/what-is-linked-data/ http://linkeddata.org
  5. 5. Semantic Web/Linked Data Technologies? A stack of technologies and languages – the semantic web layer cake – more or less fromTim Berners Lee (W3C, various sources)
  6. 6. Semantic Web/Linked Data Technologies? Oh… look another one
  7. 7. Semantic Web/Linked Data Technologies? And another…
  8. 8. Semantic Web/Linked Data Technologies? And another… (from Benjamin Nowack)
  9. 9. A Stack more like this one:
  10. 10. The Internet Network protocols to connect machines The Web Network of documents connected by hyperlinks The Linked DataWeb Graph of data objects connected by labelled hyperlinks
  11. 11. The Internet Computer level communication The Web Browsing, reading, searching The Linked DataWeb Data exchange and mashups
  12. 12. Linked Data Open University Website Open University VLE Mathieu’s Homepage Mathieu’s List of Publications Mathieu’s Twitter The Web M366 Course page Person: Mathieu Publication:Pub1 Organisation: The Open University Course: M366 Country: Belgium Book: Mechatronics author workFor availableIn offers setBook The Web of Linked Data
  13. 13. How that works: URIs Example: http://data.open.ac.uk/course/aa100 An anchor for linking Let’s say you took this course. You – took  this URI An identifier for a data entity Here, the a course offered by the Open University An access point to representation(s) of the data entity In possibly different formats…
  14. 14. URI resolving http://data.aalto.fi/id/courses/noppa/dept_T3030 10/09/13 15 In the browser (Accept: text/html) curl -H "Accept: application/rdf+xml" -L http://data.aalto.fi/id/courses/noppa/dept_T3030 <rdf:Description rdf:about="http://data.aalto.fi/data/id/courses/noppa/dept_T3030"> <rdfs:label>RDF description of Department of Media Technology</rdfs:label> <foaf:primaryTopic> <aiiso:Department rdf:about="http://data.aalto.fi/id/courses/noppa/dept_T3030"> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5077"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.2211"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.3101"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5100"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5006"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.1300"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5600"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4950"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.1100"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.6596"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5300"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.1220"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.4360"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5701"/> <aiiso:code>T3030</aiiso:code> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4210"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5070"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4400"/> <foaf:name xml:lang="en">Department of Media Technology</foaf:name> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5310"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5020"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.1110"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.6595"/>
  15. 15. How that works: Graph Data modelling (RDF) http://data.open.ac.uk/course/aa100 “The arts past and present” http://data.open.ac.uk/saou/ontology#undergraduate http://purl.org/vocab/aiiso/schema#Module http://data.open.ac.uk/topic/arts_and_humanities http://sws.geonames.org/3017382/ “France” dc:title rdf:label rdf:type dc:subject courseLevel geo:lat geo:long location
  16. 16. How that works: Querying over HTTP - SPARQL select distinct ?q (count(distinct ?t) as ?n) where { ?q a <http://purl.org/net/mlo/qualification>. ?q <http://data.open.ac.uk/saou/ontology#hasPathway> ?p. ?p <http://data.open.ac.uk/saou/ontology#hasStage> ?s. {{?s <http://data.open.ac.uk/saou/ontology#includesCompulsoryCourse> ?c} union {?s <http://data.open.ac.uk/saou/ontology#includesOptionalCourse> ?c}}. ?c <http://purl.org/dc/terms/subject> ?t. [] <http://www.w3.org/2004/02/skos/core#hasTopConcept> ?t. } group by ?q order by desc(?n) List of courses (degrees, etc.) atThe Open University, with number of topics they cover Example: data.open.ac.uk/query URI of the query: http://data.open.ac.uk/query?query=select%20distinct%20...
  17. 17. Applications Resource Discovery Research Exploration Social
  18. 18. Simple example Interactive map of Open University Buildings in the UK
  19. 19. Spaces Floors ID Address Post- code Buildings build1 build1- address Postcode- mk76aa name “Berrill building” data.open.ac.uk Milton Keynes inDistrict Buckingh amshire inCounty Mk76aa- location location lat long 52.024924 -0.709726 data.ordnancesurvey.co.uk
  20. 20. Another application Location of students showing particular interest based on their enrolment into courses
  21. 21. Same thing? Not exactly ID course post- code Students Stays private data.open.ac.uk Topics data.ordnancesurvey.co.uk Districts Location Clustering Other resources DBpedia Geonames
  22. 22. Analysing own data agains others Academics in “Arts and Humanities” most often involved with the media (in number of news items) Topics most commonly mentioned by news outlets own by the BBC (in number of news items) From news clipping data From dataset about our researchers From dbpedia.org
  23. 23. ParkJam http://parking.kmi.open.ac.uk/ ParkJam is a mobile app for Android™ that gets parking availability information from its users, so that we can all conveniently find parking when coming to work or driving into town. When you find some car park is full, it's real easy to tell others about it.
  24. 24. Study at the OU mobile app
  25. 25. And more… OU course material on mobile platform Social connection through courses
  26. 26. Data Linked Data The SemanticWeb
  27. 27. The Web Network of documents connected by hyperlinks The Linked DataWeb Graph of data objects connected by labelled hyperlinks The SemanticWeb Connected knowledge where entities, concrete and abstract, have formal attached meaning/interpretations
  28. 28. The SemanticWeb Smart, knowledge intensive, connected systems The Web Browsing, reading, searching The Linked DataWeb Data exchange and mashups
  29. 29. Gene Ontology FMA Ontology LODE BIBO Geo Ontology DBPedia Ontology Dublin Core FOAF DOAP SIOC Music Ontology Media Ontology rNews Ontologies
  30. 30. Example: Research project in the history of reading
  31. 31. Experience Person Document Event Location City Country date: Date subClassOf subClassOf locatedIn readerInvolved textInvolved givesBackgroundTo title: String description: String published: Date creator/editor providesExcerptFor occupation religion originCountry gender LinkedEvent Ontology CITO Citation Ontology Dublin Core FOAF DBPedia
  32. 32. Tracking a specific context/topic through ontology-based querying Looking at reading, by military staff during the first world war
  33. 33. Example: Generic analytics, taking into account background knowledge in the domain Web logs or application logs Web logs or application logs Web logs or application logs Generic Ontology of events, resources and actions Domain specific extension ontology (= background knowledge) Analytics with domain specific filters, views and reasoning
  34. 34. Example in learning analytics
  35. 35. Generic ontology
  36. 36. Other use in Personal analytics based on log integration (see http://uciad.info)
  37. 37. More complex reasoning: Ontological+epistemic inference on Facebook • Screenshot
  38. 38. Facebook graph API Basic linked data Facebook Ontology Ontological inference (types, relations) Epistemic logic theory of Facebook Epistemic inference (who knows what)
  39. 39. Facebook Ontology (extract) Person Post Photo Video Status update Comment Agent App subclass author likes includes subclass author on Place in {Everyone, Friends_of_Friends,All_Friends, Custom} scope
  40. 40. Example epistemic rules Ka Post(X) :- author(X, a) Ka Post(X) :- scope(X,All_Friends), author(X,Y), friend(Y, a) Ka Post(X) :- includes(X,Y), friend(Y, a) Ka wasIn(P,Y) :- includes(X,Y), in(X,P), Ka Post(X) Ka wasWith (Y,Z) :- includes(X,Y), include(X,Z), Ka Post(X)
  41. 41. Data/Information/Knowledge on the Semantic Web NLP Information retrieval Recommender Systems Data Mining Step further: intelligent applications and knowledge discovery
  42. 42. The Linked DataWeb Graph of data objects connected by labelled hyperlinks The SemanticWeb Connected knowledge where entities, concrete and abstract, have formal attached meaning/interpretations IntelligentWeb information and knowledge processing Discovering knowledge models
  43. 43. Simple example: graph analysis for data integration
  44. 44. Combining Structured and Unstructured Information: DiscOU (http://discou.info)
  45. 45. data.open.ac.uk Semantic Indexing Semantic Index Named Entity Recognition Podcasts, OpenLearn Units and Articles Semantic Entities (Dbpedia) Indexes BBC Programme or iPlayer page Synopsis Similarity- Based Search Indexes Interface Resource descriptions Resources URIs + common topics
  46. 46. Same thing, with just text (discou.info/alfa)
  47. 47. And on course material
  48. 48. PowerAqua: Question Answering
  49. 49. Finding patterns in data: Data mining Example: Using Formal Concept Analysis + Reasoning to build a hierarchy of questions a linked dataset can answer Use statistical metrics to identify the ones that are most likely to be interesting
  50. 50. Using Linked Data for Interpreting data patterns
  51. 51. Example:Analysing patient pathways annotated with a french classification, and exploring the results with ICD-10
  52. 52. Step further: Understanding knowledge representation and data modeling The SemanticWeb also represents a very large, collaborative base of formally represented knowledge This can also be mined, to discover things about knowledge representation and data modeling
  53. 53. KMi Watson
  54. 54. Architecture (a Semantic Web Search Engine)
  55. 55. Interface
  56. 56. Watson as a Service ProvidingWeb accessible APIs to a collection of online ontologies and semantic data sources
  57. 57. PowerAqua: Question Answering
  58. 58. Ontologies on the Semantic Web Number of entities Domain covered Underlying description logic
  59. 59. 21 different ontologies with a SeaFood concept Agreement Disagreement
  60. 60. http://uciad.info SeaFood disjointWith Meat SeaFood subClassOf Meat
  61. 61. Using consensus to assess an ontology (a new NeOn toolkit plugin AKT Portal The brighter the blue the higher the positive consensus (higher agreement) The brighter the red the lower the negative consensus (higher disagreement)  Dark = controversy: no clear cut between disagreement and agreement Example: The statements attached to the class Employee are controversial: some ontologies agree, others disagree (often due to alternative representations of roles)
  62. 62. Summary Intelligent information processing The Semantic Web Linked Data Web The Web Internet Making smart thing with what we can find in the web Naturally integrated data, flexible model for rapid development Large scale, collaborative, distributed, uncontrolled Connected, decentralised, independent
  63. 63. Future  Understand this Make explicit the competence of data in being used at the upper level, what is being done to it when going from raw to processed. Formalise the practice level in addition to the symbol, syntax and semantic levels, to boost development benefits. Create generic, standard processes for the development of intelligence semantic web systems.
  64. 64. ThankYou! More at: http://people.kmi.open.ac.uk/mathieu http://mdaquin.net m.daquin@open.ac.uk @mdaquin These slides at: http://slideshare.net/mdaquin Thanks to: ENRICO MOTTA FOUAD ZABLITH CARLO ALLOCCA SALMAN ELAHI KEERTHI THOMAS ILARIA TIDDI ENRICO DAGA ALESSANDRO ADAMOU