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Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling

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See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.

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Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling

  1. 1. Andreas Blumauer CEO, Semantic Web Company Dr Ian Piper UK Director PoolParty Semantic Suite WEBINAR Feb 15, 2017 Taxonomies and Ontologies The Yin and Yang of Knowledge Modelling 1
  2. 2. INTRODUCTION 2 Semantic Web Company founder & CEO of Andreas Blumauer developer and vendor of 2004 founded 5.7 current Version active at based on Vienna located part of Taxonomies Knowledge Graphs managed with standard for part of is a >200serves customers
  3. 3. 3INTRODUCTION
  4. 4. AGENDA PART 1 (Andreas Blumauer) ● Introducing the Yin and Yang of Knowledge Modelling ● Semiotic Triangle: implicit and explicit semantics ● Knowledge Acquisition Bottleneck ● Anatomy of a Knowledge Graph PART 2 (Ian Piper) ● Modelling knowledge with taxonomies and ontologies ● Building content knowledge graphs 4
  5. 5. Types of Knowledge models Implicit and explicit Semantics 5
  6. 6. Yin and Yang of Semantic Knowledge Modelling 4 Yin Yang Passive/female principle in nature Active/male principle in nature Receive information and classify Define world and reason about it Taxonomies Ontologies/Rules Implicit semantics Explicit semantics Search for ‘taxonomist’ on LinkedIn → ⅔ of found persons are female Search for ‘ontologist’ on LinkedIn → ⅔ of found persons are male Better to let them work together!
  7. 7. Implicit Semantics ▸ Natural languages ▸ Ambiguity versus Universality ▸ Context information and background knowledge needed 7 Susan observes Mike on a tower with a telescope.
  8. 8. Context is King ▸ Natural languages ▸ Ambiguity versus Universality ▸ Context information and background knowledge needed 8 - Susan and Mike are persons. - Yesterday Michael bought a Celestron. - If one buys something, (s)he owns it and can use it. - Mike and Michael is the same person. - A Celestron is a telescope. Susan observes Mike on a tower with a telescope.
  9. 9. Semiotic Triangle The level of efficiency of an Interpretant depends mainly on its ability to correctly link a symbol with the object it stands for. 9 Telescope Symbol Object Interpretant
  10. 10. Semiotic Triangle The level of efficiency of an Interpretant depends mainly on its ability to correctly link a symbol with the object it stands for. 10 Telescope Symbol Object Interpretant http://dbpedia.org/ resource/Telescope
  11. 11. How can various Knowledge Modellers build together Strong Artificial Intelligence? 11 Natural languages Taxonomies Schemas/Ontologies Statisticalmodels Computational Linguists Taxonomists DataScientists Ontologists
  12. 12. Knowledge Acquisition Bottleneck Computer (networks) need to be programmed with sufficient amount of knowledge before it can begin to learn semi-automatically 12 Knowledge Domain Knowledge Modellers Knowledge Model semantic gap Domain Experts
  13. 13. How does nature go around similar learning bottlenecks? 13 Bla bla bla bla. Bla bla bla bla The stove is on. The stove is hot! Ontological model → reasoningTaxonomical model → is-a abstractions Bla stove bla bla. Bla bla bla hot Switched on devices are dangerous devices. Switched on devices are dangerous, only if the operating temperature is above 100 degrees and the automatic shutdown mechanism is broken. The stove is on. The stove is hot! Statistical model/cooccurences → is related The stove is on. The stove is hot! Bla bla bla bla Bla bla bla bla.
  14. 14. Co-occurence model 14 Reference Corpus - Websites - PDF, Word, … - Abstracts from DBpedia - RSS Feeds Term 8 Term 3 Term 7 Term 8 Term 6 Term 9 Term 5 Term 10 - Relevant terms and phrases - Relevancy of terms - co-occurence between terms and terms Term 1 Term 4 Term 2
  15. 15. Introducing some explicit semantics ▸ Taxonomies ▸ SKOS taxonomies are concept and resource-based knowledge models 15 skos: Concept Celestron skos:prefLabel skos: Concept skos:related Mike skos:prefLabel Michael skos:altLabel skos: Concept Susan skos:prefLabel skos:related skos: Concept Scheme skos:inScheme skos:inScheme Person skos:prefLabel skos: Concept Tower of Babel skos:prefLabel skos: Concept skos:broader Telescope skos:prefLabel skos:related
  16. 16. Corpus analysis results in a network of concepts and terms 16 I need support to continuously extend our taxonomy / controlled vocabulary! skos: Concept Reference Corpus - Websites - PDF, Word, … - Abstracts from DBpedia - RSS Feeds skos: Concept skos: Concept Term 1 Term 3 Term 7 Term 8 Term 6 Term 4 Term 2 Term 5 - Relevant terms and phrases - Relevancy of concepts - co-occurence between concepts and terms - co-occurence between terms and terms
  17. 17. PoolParty The Combination of Machine Learning & Human Intelligence Content Manager Integrator Taxonomist/ Ontologist Thesaurus Server Extractor PowerTagging uses API is user of is user of is basis of is basis of Index annotates enriches Corpus Learning/ Semantic Analysis CMS extends is basis of analyzes uses API 17
  18. 18. Use co-occurences between concepts and terms to extract ‘shadow concepts’ 18 This site is a 15th-century Inca site located 2,430 metres above sea level. It is located in Cusco, Peru. It is situated on a mountain ridge above the Sacred Valley through which the Urubamba River flows. Most archaeologists believe that it was built as an estate for the Inca emperor Pachacuti. Often mistakenly referred to as the "Lost City of the Incas", it is the most familiar icon of Inca civilization. The Incas built the estate around 1450, but abandoned it a century later at the time of the Spanish Conquest. Inca site Machu Picchu Cusco Inca empire Inca emperor Peru Spanish Conquest Sacred Valley Chankas Lost City Pachacuti In addition to explicitly used concepts and terms, Machu Picchu is extracted from the article as a Shadow Concept. As a prerequisite, one has to provide and analyze a representative text corpus first. Example:
  19. 19. From taxonomies to ontologies 19 my: concepts#1 Susan skos:prefLabel skos: Conceptrdf:type ‘2017-02-15’ dct:modified my: persons#1 dc:creator foaf: Person rdf:type Alex foaf:name
  20. 20. Ontologies: Some more explicit semantics ▸ Ontologies ▸ Ontologies classify things and define more specific relations and attributes ▸ Locally and globally recognised ontologies can be combined ▸ Ontologies can have various levels of expressivity (RDFS, OWL)20 schema: Product Telescope schema:name foaf: Person schema:owns Mike foaf:nick Michael foaf:givenName foaf: Person Susan foaf:givenName myOnt:observes geo: Spatial Thing Tower of Babel skos:prefLabel schema: Brand schema:brand Celestron schema:name myOnt:visits
  21. 21. Reasoning 21 If someone buys a Celestron, (s)he can use it as a telescope. buys uses is ais subproperty of
  22. 22. Reasoning over SKOS taxonomies using OWL 22 Celestron Telescope Optical device NEXSTAR SLT Take your explorations to new heights with Celestron's NexStarSLT. Available with a variety of optical tubes up to 127 mm in aperture, the NexStar SLT has something for everyone. Beginners will appreciate the intuive SkyAlign technology, which makes aligning your device's computer to the night sky as easy as centering three bright objects in the eyepiece. The NexStar SLT is a precision instrument that can grow with you in the hobby of amateur astronomy for years to come. I’m looking for documents about Optical Devices skos:broader skos:broader is a owl:TransitiveProperty
  23. 23. Reasoning over SKOS taxonomies using SPARQL 1.1 property paths More performant! See also: SHACL 23 Celestron Telescope Optical device NEXSTAR SLT Take your explorations to new heights with Celestron's NexStarSLT. Available with a variety of optical tubes up to 127 mm in aperture, the NexStar SLT has something for everyone. Beginners will appreciate the intuive SkyAlign technology, which makes aligning your device's computer to the night sky as easy as centering three bright objects in the eyepiece. The NexStar SLT is a precision instrument that can grow with you in the hobby of amateur astronomy for years to come. I’m looking for documents about Optical Devices skos:broader …. WHERE ?s skos:broader+ ?o …..
  24. 24. Combine SKOS-XL with ontologies ▸ Use custom relations between SKOS-XL labels24 skos-xl: Label Switzerland@en skos-xl: Label Swiss Confederation@en skos-xl:altLabel my:isPredecessor geo: Spatial Thing skos-xl:prefLabel
  25. 25. Building Knowledge Graphs Anatomy of an Enterprise Knowledge Graph 25
  26. 26. Instance data prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum
  27. 27. Schema data prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image
  28. 28. CC BY-SA 3.0 Metadata prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours
  29. 29. CC BY-SA 3.0 Taxonomies and Thesauri prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day prefLabel Piazza altLabel Town Square broader related related opening Hours
  30. 30. CC BY-SA 3.0 Links between internal and external data prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day prefLabel Piazza altLabel Town Square broader related related same as opening Hours
  31. 31. The Peggy Guggenheim Collection is a modern art museum on the Grand Canal in the Dorsoduro sestiere of Venice, Italy. same as CC BY-SA 3.0 Mappings to data and documents stored in other systems prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day prefLabel Piazza altLabel Town Square broader related related opening Hours
  32. 32. Modelling knowledge Taxonomies, ontologies, linked data and structured data 32
  33. 33. Linkedin article 33 http://preview.tinyurl.com/z66vp5s
  34. 34. A taxonomy 34
  35. 35. An ontology 35
  36. 36. Geography as taxonomy 36
  37. 37. Plant taxonomy and ontology 37
  38. 38. Modelling plants in a taxonomy 38
  39. 39. A taxonomy in PoolParty 39
  40. 40. Ontology of plant ranks 40
  41. 41. PoolParty’s ontology and custom schema management 41 Taxonomy Ontology Ontology 1 from library Ontology 2 (imported) Ontology 3 (custom-made) Custom Schema
  42. 42. A custom ontology in PoolParty 42
  43. 43. 43
  44. 44. 44
  45. 45. Yin and yang 45 Modelling knowledge in PoolParty gives the best of both worlds: ● Usability of taxonomy design ● Flexibility of ontology design
  46. 46. Content knowledge graphs 46 How taxonomies can work with structured content
  47. 47. Simple content object structure 47
  48. 48. Content model as a graph 48
  49. 49. Simplified DITA model 49
  50. 50. Building a content knowledge graph - step 1 50
  51. 51. Building a content knowledge graph - step 2 51
  52. 52. 52
  53. 53. 53
  54. 54. 54
  55. 55. 55
  56. 56. Content knowledge graphs: summary 56 A content knowledge graph approach: ● Allows separation of concerns and reduces dependencies ● Is a major step in development of an enterprise knowledge graph ● Provides an incremental route from current state ● Illustrates the benefits of the Yin and Yang of taxonomies and ontologies
  57. 57. Meet the PoolParty Team at some major events in 2017 57 June 12-14, London MarkLogic World 2017 EMEA > More information Sep 11-14, Amsterdam 13th Int. Conference on Semantic Systems > More information Nov 6-9, Washington D.C. KM World and Taxonomy Bootcamp > More information Oct 17-18, London Taxonomy Bootcamp > More information Oct 21-25, Vienna 16th Int. Semantic Web Conference > More information
  58. 58. PoolParty Academy Get certified! 58 https://www.poolparty.biz/academy/
  59. 59. CONNECT Andreas Blumauer CEO, Semantic Web Company ▸ a.blumauer@semantic-web.at ▸ http://at.linkedin.com/in/andreasblumauer ▸ https://twitter.com/semwebcompany ▸ https://ablvienna.wordpress.com/ 59 © Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/
  60. 60. CONNECT Dr Ian Piper UK PoolParty Director Tellura Information Services Ltd. ▸ i.piper@semantic-web.at ▸ https://www.linkedin.com/in/ianpiper ▸ https://twitter.com/tellura_tweets ▸ http://tellura.co.uk/ 60 © Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/

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