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Ausleihdaten aus Bibliothekenals Linked Open Data publizieren und nutzen  Publishing and consuming library loan    informa...
Overview    Motivation: Use cases    Types of data    Loan transaction data    Modelling the data in RDF         Thin...
Use case: Retrieval    Loans as a quality indicator         More loans → more interest → higher relevance    „Loan rele...
Retrieval: Example 29.11.2011     Semantic Web in Bibliotheken   4
Implemenation ideas    Aggregation         Aggregate loan data on item level         Normalize loan data from different...
Use case: Resource discovery    Assumption: Items loaned together are correlated         May not hold true in all instan...
Resource discovery: Examples29.11.2011     Semantic Web in Bibliotheken   7
Implementation ideas    Analysing loan history         Same patron         Similar start of loan                Or: ov...
Types of data in library information systems29.11.2011            Semantic Web in Bibliotheken    9
Types of data: master data    Properties         Changes and edit are rare         Slowly growing dataset         Stab...
Types of data: dynamic data    Properties         Subject to changes         Quickly growing dataset         Usually a...
Loan transaction: ILS view    Bibliografic entry                            Patron information         Series          ...
Loan transaction: ILS data    Current loan         User ID         Item ID         Timestamps (start)                ...
Loan transaction: ILS data    Completed loan         As before         Timestamps (end)                Item returned b...
Modelling loan transaction data in RDF29.11.2011               Semantic Web in Bibliotheken   15
Approach 1: Consider the data    Loans as events    Minimum elements         Start Time         End Time         Item...
Event-based model                    hasItem      Titel-IRI                         Item-IRI                          hasL...
Properties    Easy implementation         Existing data can be used 1:1    Highly granular data         Each loan even...
Approach 2: consider the application    Loans as statistics         Loans per year / month / week         Differentiati...
Statistics-based model                        hasItem          Titel-IRI                       Item-IRI                   ...
Properties    Harder implementation         Need to decide on statistical units         Need to normalize different loa...
So thats it?    University of Huddersfield Library (UK)         Published circulation data as open data            http...
Loan conditions at UB Mannheim    Closed stacks         Orders or hold requests from the OPAC         4 weeks default l...
Normalizing loan data    Different default periods         Is a 8 week loan “more” than a two week loan?    Multiple ho...
Model revisited                                                          ownsTitle                                      Ti...
Model revisited                                                          ownsTitle                                      Ti...
Modelling title correlation29.11.2011         Semantic Web in Bibliotheken   27
Correlation model    Correlation connects two titles         Type and strength of connection differ                    T...
Implementation    Aggregation based on RDF published events possible         But extremely complex         Anonymized d...
Conclusion29.11.2011   Semantic Web in Bibliotheken   30
Conclusion    There are evident usage scenarios for loan data         Retrieval         Resource Discovery    Comparin...
Looking ahead    We are currently         Creating a complete vocabulary definition         Converting several months o...
And even further...    Presentation         We are thinking about a Javascript semantic plugin                For brows...
Thank you for listening.29.11.2011        Semantic Web in Bibliotheken   34
Discussion                  ?29.11.2011   Semantic Web in Bibliotheken   35
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Ausleihdaten aus Bibliotheken als Linked Open Data publizieren und nutzen

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Vortrag auf der SWIB11 Semantic Web in Bibliotheken Konferenz 28.11. bis 30.11.2011 in Hamburg.

Abstract:
In einem laufenden Projekt untersuche ich gemeinsam mit Kai Eckert von der UB Mannheim und Studierenden an der Hochschule der Medien mögliche Anwendungsfälle für Ausleihdaten aus Bibliothekssystemen als Linked Data.

Use cases sind die Unterstützung im Retrieval und Resource Discovery. Ausleihen können zum einen als Qualitätssignal interpretiert werden und für das Ranking oder die Sortierung verwendet werden. Zum anderen entsteht durch das gemeinsame Ausleihen von Medien ein Indikator für die Zusammengehörigkeit dieser Medien, was für die Anzeige von interessanten Titeln verwendet werden kann.

Die Herausforderung bei der Modellierung in RDF sind die verschiedenen denkbaren Granularitäten, in denen die Daten aufbereitet werden. So ist es möglich, jeden einzelnen Ausleihvorgang zu modellieren und zu beschreiben. Dabei würden nahezu alle Informationen erhalten bleiben, die im Bibliothekssystem erfasst wurden. Für die Anwendungsfälle wäre aber eine gröbere Abbildung, die die Daten auf Titelebene aggregiert, wünschenswert.

Derzeit entstehen die Datenmodelle für die genannten Szenarien und eine beispielhafte Abbildung von Echtdaten aus dem Bibliothekssystem der UB Mannheim. Wir wollen die Modellierung und die Echtdaten im Laufe der nächsten Monate auf dem Linked Data Service der UB Mannheim bereitstellen.

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Transcript of "Ausleihdaten aus Bibliotheken als Linked Open Data publizieren und nutzen"

  1. 1. Ausleihdaten aus Bibliothekenals Linked Open Data publizieren und nutzen Publishing and consuming library loan information as linked open data Prof. Magnus Pfeffer, Stuttgart Media University pfeffer@hdm-stuttgart.de
  2. 2. Overview Motivation: Use cases Types of data Loan transaction data Modelling the data in RDF  Thinking from data  Thinking from usage Conclusion Looking ahead 29.11.2011 Semantic Web in Bibliotheken 2
  3. 3. Use case: Retrieval Loans as a quality indicator  More loans → more interest → higher relevance „Loan relevance“ is orthogonal to traditional relevance criteria  Desireable with result sets of almost identical content  Example search:  „Introduction to algebra“  „Mathematics for students of social sciences“ 29.11.2011 Semantic Web in Bibliotheken 3
  4. 4. Retrieval: Example 29.11.2011 Semantic Web in Bibliotheken 4
  5. 5. Implemenation ideas Aggregation  Aggregate loan data on item level  Normalize loan data from different locations  Aggregate loan data on title level User interface  Displaying loan statistics in the short result list  Sort short result list by loan statistics  Using loan statistics in ranking 29.11.2011 Semantic Web in Bibliotheken 5
  6. 6. Use case: Resource discovery Assumption: Items loaned together are correlated  May not hold true in all instances  But certainly on an aggregated level Present „similar titles“ based on correlation information 29.11.2011 Semantic Web in Bibliotheken 6
  7. 7. Resource discovery: Examples29.11.2011 Semantic Web in Bibliotheken 7
  8. 8. Implementation ideas Analysing loan history  Same patron  Similar start of loan  Or: overlapping loan periods → Groups of titles → pairs of correlated titles Aggregation  Summation of correlation counts  Generation of correlation groups for each title Presentation  Top-n most correlated titles  Consider minumum correlation to suppres spurious results 29.11.2011 Semantic Web in Bibliotheken 8
  9. 9. Types of data in library information systems29.11.2011 Semantic Web in Bibliotheken 9
  10. 10. Types of data: master data Properties  Changes and edit are rare  Slowly growing dataset  Stable identifiers Examples:  Business information systems  Product information  Customer contact information  Vendor information  Library information systems  Catalogue entries  Patron contact information 29.11.2011 Semantic Web in Bibliotheken 10
  11. 11. Types of data: dynamic data Properties  Subject to changes  Quickly growing dataset  Usually a combination of master data entries  Usually no own identifier Examples:  Business information systems  Sales transaction details  Library information systems  Media purchase transaction details 29.11.2011 Semantic Web in Bibliotheken 11
  12. 12. Loan transaction: ILS view Bibliografic entry  Patron information  Series  Name  Author  Adress  Title, … k  User type C hec  Item information  ID / Barcode  Item type  Call number  Location  ID / Barcode Loan transaction 29.11.2011 Semantic Web in Bibliotheken 12
  13. 13. Loan transaction: ILS data Current loan  User ID  Item ID  Timestamps (start)  Order / Hold request  Pickup ready  Pickup by patron  Loan due time  Loan extensions  Timestamps / Numbers  Overdue escalation / overdue messages sent 29.11.2011 Semantic Web in Bibliotheken 13
  14. 14. Loan transaction: ILS data Completed loan  As before  Timestamps (end)  Item returned by patron  Return to stacks Privacy To protect the privacy of the patrons, the information on completed loans is usualy anonymised after a short period of time. 29.11.2011 Semantic Web in Bibliotheken 14
  15. 15. Modelling loan transaction data in RDF29.11.2011 Semantic Web in Bibliotheken 15
  16. 16. Approach 1: Consider the data Loans as events Minimum elements  Start Time  End Time  Item ID  Anonymised User ID or User Type Additional elements  Differentiated timestamps  Number of extensions 29.11.2011 Semantic Web in Bibliotheken 16
  17. 17. Event-based model hasItem Titel-IRI Item-IRI hasLoanEvent LoanEvent-IRI Properties HoldRequestDate PickupDate ReturnDate NoOfExtensions Inverse relations DueDate should be included in User ID the vocabulary and are left out for readability.29.11.2011 Semantic Web in Bibliotheken 17
  18. 18. Properties Easy implementation  Existing data can be used 1:1 Highly granular data  Each loan event needs an individual IRI  RDF consumers need to aggregate data themselves → complex graphs → costly queries 29.11.2011 Semantic Web in Bibliotheken 18
  19. 19. Approach 2: consider the application Loans as statistics  Loans per year / month / week  Differentiation by user type or location 29.11.2011 Semantic Web in Bibliotheken 19
  20. 20. Statistics-based model hasItem Titel-IRI Item-IRI hasLoanStatistics LoanStatistics-IRI Properties Time period Number UserGroup 29.11.2011 Semantic Web in Bibliotheken 20
  21. 21. Properties Harder implementation  Need to decide on statistical units  Need to normalize different loan conditions Less granular data  Less IRIs  Simple queries  Information on correlated titles is lost 29.11.2011 Semantic Web in Bibliotheken 21
  22. 22. So thats it? University of Huddersfield Library (UK)  Published circulation data as open data  http://library.hud.ac.uk/data/usagedata/  Used in a semantic catalog prototype 29.11.2011 Semantic Web in Bibliotheken 22
  23. 23. Loan conditions at UB Mannheim Closed stacks  Orders or hold requests from the OPAC  4 weeks default loan period And there are  Extensions possible several additional 2 weeks if there are other requests collections  Textbook collection –  No online orders or hold requests with even more diverse  2 week default loan period conditions  No Extensions possible Open access areas  No online orders or hold requests  6 month default loan period – staff only 29.11.2011 Semantic Web in Bibliotheken 23
  24. 24. Normalizing loan data Different default periods  Is a 8 week loan “more” than a two week loan? Multiple hold requests on loaned items possible  These items are on loan permanently – is this the same as an item on year-long loan by a single staff patron? Loan-and-return items  Patrons cannot browse books from the closed stacks  Browsing is done on the counter and discarded items are returned promptly – should these be counted as loans? 29.11.2011 Semantic Web in Bibliotheken 24
  25. 25. Model revisited ownsTitle Titel-IRI Institution-IRI Item ID Item Type hasItem hasLocation Call number Properties atLocation Item-IRI Location-IRI hasLoanEvent hasLoanCondition HoldRequestDate LoanEvent-IRI LoanConditions-IRI PickupDate ReturnDate NoOfExtensions Properties DueDate User ID 29.11.2011 Semantic Web in Bibliotheken 25
  26. 26. Model revisited ownsTitle Titel-IRI Institution-IRI Item ID Location hasItem hasItemType Call number Properties ofItemType Item-IRI ItemType-IRI hasLoanEvent hasLoanCondition HoldRequestDate LoanEvent-IRI LoanConditions-IRI PickupDate ReturnDate NoOfExtensions Properties DueDate User ID 29.11.2011 Semantic Web in Bibliotheken 26
  27. 27. Modelling title correlation29.11.2011 Semantic Web in Bibliotheken 27
  28. 28. Correlation model Correlation connects two titles  Type and strength of connection differ Titel-IRI hasConnection hasType Connection-IRI ConnectionType-IRI Properties Titel-IRI ConnectionType ConnectionValue 29.11.2011 Semantic Web in Bibliotheken 28
  29. 29. Implementation Aggregation based on RDF published events possible  But extremely complex  Anonymized data can make it impossible → Should be published separately from loan events 29.11.2011 Semantic Web in Bibliotheken 29
  30. 30. Conclusion29.11.2011 Semantic Web in Bibliotheken 30
  31. 31. Conclusion There are evident usage scenarios for loan data  Retrieval  Resource Discovery Comparing loan statistics between institutions is hard  Wildly diverging loan conditions based on user and item type and/or location  Little consensus in the community Modelling in RDF is possible  Complex model with many IRIs  The model theoretically satisfies both use cases  But for performance and privacy reasons loan data and correlation data should be published independently 29.11.2011 Semantic Web in Bibliotheken 31
  32. 32. Looking ahead We are currently  Creating a complete vocabulary definition  Converting several months of loan data for UB Mannheim  As granular events  As aggregated sums according to the german library statistics (DBS ) We will  Evaluate the run-time complexity of the aggregation based on RDF  Create and publish correlation scores based on circulation data mining 29.11.2011 Semantic Web in Bibliotheken 32
  33. 33. And even further... Presentation  We are thinking about a Javascript semantic plugin  For browsers or embedding into OPAC pages  Show links, aggregate additional information, etc. Data  Loan data is interesting, but relatively spars  The real action happens in the OPAC  Interest indicators from clicks to full view  Correlation data from search sessions  Harvesting this information is possible by anonymous session tracking 29.11.2011 Semantic Web in Bibliotheken 33
  34. 34. Thank you for listening.29.11.2011 Semantic Web in Bibliotheken 34
  35. 35. Discussion ?29.11.2011 Semantic Web in Bibliotheken 35
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