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Museum Recommender using Semantic Web
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Museum Recommender using Semantic Web

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Objective …

Objective
The purpose of this project is to find and recommend all museums which are located in the user’s city and contain the user’s preferred artist and artistic period. The program takes as an input from the user his or her current location, one preferred artist and one preferred artistic period.
If the given artist does not belong to the given artistic period, there are two possible types of recommendations:
The best recommendation represents one or more museums which contain both the preferred artist and the artists belonging to the preferred artistic period. If no such museums are found in the user’s city, then the second best recommendation generates museums in that city, that either contain the preferred artist’s work or museums that only contain works from the preferred artistic period.
Implementation
The program is implemented in Java and it queries Dbpedia, using two queries. First, the user is asked to enter the location, artist and period with the help of a scanner. Then, there are two queries which use this input in order to find museums that match the user’s profile and are located in the given city. For the first query, the artist and period are intersected, in order to find museums that contain both. If there are no results for this query, the second one is run, which contains a union, finding museums that either contain the artist or the period.
Finally, the resource page of the matched museums is printed on the screen. In parallel, a set of triples is exported into a file, using the Museum Recommender Ontology. For each type of recommendation, the ontology specifies whether the match is generated based on an intersection or a union.

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Transcript

  • 1. Museum Recommender Mara Dumitru
  • 2. Museum Visits while Traveling • Choose National Museum: – General overview – Many different painters and styles – Overlook favorite artists or artistic period
  • 3. Purpose• For art lovers• Recommend museums in the users’ city based on their preferred artist and artistic period• Two types of recommendations: – The best recommendation finds museums that contain both the given artist’s work and the works of artists belonging to the preferred artistic period – The second best recommendation finds museums that only contain works of either one of the two preferences
  • 4. Method• 3 user inputs as strings which are transformed into URIs – current city – favorite artist – favorite artistic period• Include the strings in the queries for DBpedia• Generate URIs of museums, based on input (i.e. user preferences)
  • 5. Ontology
  • 6. Implementation• 2 SPARQL queries which select museums: – Intersect artist and period – Union to include results from either one• Implemented in Java (Jena library)• Prints to file a set of RDF triples, describing the recommendation and its type, using the URIs and the ontology
  • 7. Demo
  • 8. Advantages• Even if the period the artist belongs to does not match the preferred period… Favorite artist: Jan van Eyck (Period: Renaissance) Favorite period: Impressionism
  • 9. Demo
  • 10. Drawbacks (DBpedia)• Missing information• Limits the number of links between artists, periods and museums• Not enough properties, such as painting theme
  • 11. Semantic Web• Enables the implementation of recommendation programs that analyze and select suggestions, based on a user profile.• Web interaction becomes more personalized and more precise, with the increasing number of databases and querying methods.• Museum Recommender: simple preferences are retrieved in DBpedia and connected to each other, resulting in suggestions which are only suitable for the user.
  • 12. Semantic Web• Uses SPARQL to query for museums in DBpedia.• Includes recommendation types as OWL individuals, in order to better define the RDF triples.
  • 13. Future Implementations• Museum Recommender as a mobile application – especially practical for short travels – generates museums that contain user’s preferred artists and periods – recommends museums in a fast and personalized manner, anywhere in the world
  • 14. How to Improve• Find cities near the user’s given location that represent similar or better recommendations for museums.• Add the theme (landscape, portrait, historical painting, etc.) as one of the user’s inputs and include this preference in the recommendation.