This document summarizes the CHIP project, which aims to use semantic metadata about cultural heritage objects to improve personalized access and recommendations for museum visitors. The CHIP approach involves making metadata and vocabularies available as RDF/OWL, aligning and enriching the data, and using it to build a combined user model for generating virtual and physical museum tours. Experiments show semantic relations can enhance content-based recommendations for novices and experts. Follow-up projects include Agora, deploying the techniques at the Rijksmuseum in Amsterdam.

1. CHIP
Semantics-driven Recommendations and
Personalized Museum Tour Generation
Lora Aroyo

2. Bridging the Gap:
Virtual and Physical Museum Experience

3. Research Questions
□ Can we use semantic
metadata of cultural
heritage to improve
personalized access
through multiple
devices (e.g. PC,
mobile phone, PDA)?

4. I: CHIP Approach to Semantics
□ Making museum metadata available in RDF/OWL
□ Making relevant vocabularies available in
RDF/OWL
□ Aligning & enriching vocabularies/metadata
□ Using resulting RDF/OWL graph for building a
combined (virtual and physical) user model
□ Using the above result for (semi)automatic
generation of virtual and physical museum tours

6. style: Baroque
teacher of: Gerrit Dou
teacher of: Nicolaes Maesteacher of: Ferdinand Bol
self-portrait
militia
place:
Amsterdam
period:
1625 to 1650

7. RDF/OWL Graph for Building
User Model
□ Users rating:
■ Artworks & artwork properties
■ 20 pre-selected by museum curators
■ rest random
□ Ex.:
■ I like “Jewish Bride”
■ I like creator “Rembrandt”
■ I like style “Baroque”

8. User Profile
□ Based on Web
standards (FOAF,
RDF)
□ Combining physical
& digital activities
from multiple
sources
□ Reusable
□ Distributed

9. Artwork Recommender

10. Serendipity in Recommendations
□ Recommendations based on explicit semantics (for
reusability)
□ Cold-start problem improved
□ User control in what to consider for recommendations
□ Non-trivial related recommendations
□ Faceted browsing
□ Combination of CB and collaborative aspects

11. Artwork
Properties

12. Property
Details
(artist)

13. II: CHIP Approach to the Bridging

14. Using RDF/OWL User Model for
Generation of Museum Tours
□ Generation of Tours, e.g.
“Masterpieces”, “Favorites”, etc.:
□ positively rated creators
□ related artists, i.e. collaborator & student_of
□ same style & style siblings
□ …
□ topical tours
□ length settings
□ spatial adaptation

15. Tour Wizard

16. Mobile Museum Guide

17. III: Domain & User-centered
Approach
□ Four experiments:
■ I: Recommendations effectiveness: novices vs. experts; AR
helps novices to elicit/clarify preferences from their implicit
knowledge of the collection.
■ II: Rating, sparsity and cold-start; "expert-sorted" sequence of
artworks works very well for first-time users to build user profiles;
"rating artwork and topics" increases the total amount of the user's
contributions and improves precision of recommendations.
■ III: Accuracy and interestingness of recommendations;
semantic relations can enhance the CB recommendations.
■ IV: Ontology-enhanced content-based & collaborative
recommendations; semantic relations do not give a significant
difference for the performance of recommender.
□ Study: interactive museum tours in NL and online guides
□ Study: in-door navigation & identification techniques
□ Planned experiments: tour adaptation with semantic & spatial
restrictions

18. Research Topics
□ Recommendations with respect to subject/theme
□ Recommendations with respect to semantic relations
□ Recommendations for groups of users
□ Taking into account temporal preferences in
recommendations, e.g. “I am not interested NOW”
□ Adapting the tour sequence on the fly
■ based on position, time, interest, previous activities
□ Adapting the artwork sequence in user profiler
■ improving the cold-start problem
□ Social aspects based on FOAF profile
□ Extension of user profile FOAF specification with
interest property

19. CHIP Tools & Architecture
□ www.chip-project.org
■ www.chip-project.org/demo
■ http://www.chip-project.org/demo/mobileguide/index.jsp
■ http://www.chip-project.org/spacechip/
■ http://www.chip-project.org/chipICITY

20. The Team
□ Lora Aroyo
□ Paul De Bra
□ Peter Gorgels
□ Cathy Jager
□ Lloyd Rutledge
□ Mettina Veenstra
□ Natalia Stash
□ Yiwen Wang

21. Follow-up (1)
□ CATCH2 Agora project
■ social platform with CH objects in explicit
(art)historic context
□ digitally mediated public history
□ historic events model for semantically
relevant relationships between objects
□ community curated historic event
thesaurus linked to museum artifacts
■ Rijksmuseum Amsterdam & Dutch
audiovisual archive Beeld en Geluid

22. Follow-up (2)
□ CATCHPlus Rijksmuseum deployment
■ Q42, Fabrique
■ CHIP API
■ CATCH Central User Profiling Service
■ Rijksmuseum “Genius” on
Rijksmuseum.nl