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From Knowledge Organization (KO) to Knowledge Representation (KR)

From Knowledge Organization (KO) to Knowledge Representation (KR). Fausto Giunchiglia, Biswanath Dutta, Vincenzo Maltese maltese@disi.unitn.it. 2. ISKO UK 7/10/2013. LIS developed its own very successful solutions for the classification and search of documents.

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From Knowledge Organization (KO) to Knowledge Representation (KR)

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  1. From Knowledge Organization (KO) to Knowledge Representation (KR) Fausto Giunchiglia, Biswanath Dutta, Vincenzo Maltesemaltese@disi.unitn.it

  2. 2 ISKO UK7/10/2013 LIS developed its own very successful solutions for the classification and search of documents. In KO, search is focused on document properties (e.g. title, author, subject). KO tends to fail in situations when users express their needs in terms of entity properties (e.g., of the author) Knowledge Organization: make information available

  3. 3 ISKO UK7/10/2013 Search by document propertiesGive me documents with subject “Michelangelo” and “marble sculpture” Search by entity propertiesGive me documents aboutmarble sculptures made by a person born in Italy Limitations in KO: Lack of expressiveness

  4. 4 ISKO UK7/10/2013 Subject: Buonarroti, Michelangelo Subject: sculpture – Renaissance In indexes the syntaxof the subjects is given by a subject indexing language grammar. What about the semantics? • Is Michelangelo the Italian artist? When and where he was born? What are his most famous works? • Is sculpturethe form of art? • Is Renaissancethe historical period? When and where exactly? Limitations in KO: Lack of formalization

  5. 5 ISKO UK7/10/2013 Name: David Class: statueAuthor: MichelangeloDate of creation: 1504Matter: marbleHeight: 4.10 m From documents to entities Name: Michelangelo Surname: Buonarroti Class: artistDate of birth: 6th March 1475Date of death: 18th February 1564

  6. 6 ISKO UK7/10/2013 KRsearch by any entity property KOsearch by title, author, subject From KO to KR

  7. 7 ISKO UK7/10/2013 Classification Ontologies

  8. 8 ISKO UK7/10/2013 Descriptive Ontologies: intensional knowledge

  9. 9 ISKO UK7/10/2013 Descriptive Ontologies: extensional knowledge

  10. 10 ISKO UK7/10/2013 Descriptive Ontologies: extensional knowledge Give me documents about any lake with depth greater than 100 written by Italians

  11. 11 ISKO UK7/10/2013 How to build high quality and scalable descriptive ontologies? DERA is faceted as it is inspired to the principles and canons of the faceted approach by Ranganathan DERA is a KR approach as it models entities of a domain (D) by their entity classes (E), relations (R) and attributes (A) A new approach: DERA

  12. 12 ISKO UK7/10/2013 • Step 1: Identification of the atomic concepts • (E) watercourse, stream: a natural body of running water flowing on or under the earth • Step 2: Analysis • a body of water • a flowing body of water • no fixed boundary • confined within a bed and stream banks • larger than a brook Descriptive Ontologies in DERA (I)

  13. 13 ISKO UK7/10/2013 • Step 3: Synthesis. • (E) Body of water • (is-a) Flowing body of water • (is-a) Stream • (is-a) Brook • (is-a) River • (is-a) Stillbody of water • (is-a) Pond • (is-a) Lake • Step 4: Standardization. • (E) stream, watercourse: a natural body of running water flowing on or under the earth Descriptive Ontologies in DERA (II)

  14. 14 ISKO UK7/10/2013 • Step 5: Ordering • Terms and concepts in the facets are ordered • Step 6: Formalization • Descriptive ontologies are translated into Description Logic formal ontologies, e.g.,: • River ⊑ Stream • River (Volga) • Length (Volga, 3692) Descriptive Ontologies in DERA (III)

  15. 15 ISKO UK7/10/2013 • DERA facets have explicit semantics and are modeled as descriptive ontologies • DERA facets inherits all the nice properties of the faceted approach, such as robustness and scalability • DERA allows for a very expressive document search by any entity property • DERA allows for automated reasoning via the formalization into Description Logics ontologies Properties of DERA

  16. 16 ISKO UK7/10/2013 • The usefulness of moving from KO to KR • KO is methodologically very strong, but subjects are limited in formality and expressiveness as, by employing classification ontologies, it only supports queries by document properties. • KR, by employing descriptive ontologies, supports queries by any entity property, but it is methodologically weaker than KO. • We propose the DERA faceted KR approach • DERA, being faceted, allows the development of high quality and scalable descriptive ontologies • DERA, being a KR approach, allows modeling relevant entities of the domain and their E/R/A properties and enables automated reasoning. • It supports a highly expressive search of documents exploiting entity properties. Conclusions

  17. 17 ISKO UK7/10/2013 From KO to KRThank you!

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