Faceted Exploration of Cultural Heritage

The richness of Cultural Heritage (CH) sites exposes tourists to an information overload which makes it difficult to efficiently select the items that they like and can practically visit within a tour. Faceted information exploration has been proposed as a solution to analyze large sets of data. However, most works focus on the inspection of a single type of information, e.g., hotels or music. In contrast, CH items are heterogeneous: they include natural and artificial monuments and different types of artworks which might be visited within a single tour. Moreover, CH sites are often visited in group, thus raising the expectation that all the involved people share information and decisions about what to do. In order to address this issue, we propose a map-based faceted exploration model that makes it possible to create custom, long-lasting maps representing a shared information space for user collaboration, and temporally project these maps on the basis of fine-grained filters which help users focus on items associated to short-term, specific interests. Our model supports the user in the organization and filtering of CH information on the basis of multiple perspectives related to the attributes of items. We propose graphical widgets to support interactive data visualization, faceted exploration, category-based information hiding and transparency of results at the same time. The widgets are based on the sunburst diagram, which compactly displays visualization criteria on data categories by showing facets and facet values in a circular structure.

[1]  Noemi Mauro,et al.  Semantic Interpretation of Search Queries for Personalization , 2017, UMAP.

[2]  Yannis Tzitzikas,et al.  PFSgeo: Preference-Enriched Faceted Search for Geographical Data , 2017, OTM Conferences.

[3]  Hanspeter Pfister,et al.  UpSet: Visualization of Intersecting Sets , 2014, IEEE Transactions on Visualization and Computer Graphics.

[4]  Ben Shneiderman,et al.  Visual information seeking: tight coupling of dynamic query filters with starfield displays , 1994, CHI '94.

[5]  Noemi Mauro,et al.  Faceted Search of Heterogeneous Geographic Information for Dynamic Map Projection , 2020, Inf. Process. Manag..

[6]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[7]  Noemi Mauro,et al.  Map-based visualization of 2D/3D spatial data via stylization and tuning of information emphasis , 2018, AVI.

[8]  Aniket Kittur,et al.  SearchLens: composing and capturing complex user interests for exploratory search , 2019, IUI.

[9]  Francesco Ricci,et al.  Selective contextual information acquisition in travel recommender systems , 2017, Information Technology & Tourism.

[10]  Ilknur Celik,et al.  Leveraging the Semantics of Tweets for Adaptive Faceted Search on Twitter , 2011, SEMWEB.

[11]  Diogo Cabral,et al.  Designing for Exploratory Search on Touch Devices , 2015, CHI.

[12]  Loren G. Terveen,et al.  Exploring the filter bubble: the effect of using recommender systems on content diversity , 2014, WWW.

[13]  Morten Hertzum,et al.  Visualizing the application of filters: A comparison of blocking, blurring, and colour-coding whiteboard information , 2013, Int. J. Hum. Comput. Stud..

[14]  Jerry R. Hobbs,et al.  DAML-S: Semantic Markup for Web Services , 2001, SWWS.

[15]  Bret Jackson,et al.  Cartograph: Unlocking Spatial Visualization Through Semantic Enhancement , 2017, IUI.

[16]  Noemi Mauro,et al.  OnToMap: Semantic Community Maps for Knowledge Sharing , 2017, HT.

[17]  Xue Dong Yang,et al.  A Comparative User Study of Web Search Interfaces: HotMap, Concept Highlighter, and Google , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[18]  Emmanuel Pietriga,et al.  An Evaluation of Interactive Map Comparison Techniques , 2015, CHI.

[19]  Yi Zhang,et al.  Personalized interactive faceted search , 2008, WWW.

[20]  Katrien Verbert,et al.  IntersectionExplorer, a multi-perspective approach for exploring recommendations , 2019, Int. J. Hum. Comput. Stud..

[21]  Yannis Tzitzikas,et al.  Faceted exploration of RDF/S datasets: a survey , 2017, Journal of Intelligent Information Systems.

[22]  Eyal Oren,et al.  Extending Faceted Navigation for RDF Data , 2006, SEMWEB.

[23]  Raimund Dachselt,et al.  FacetZoom: a continuous multi-scale widget for navigating hierarchical metadata , 2008, CHI.

[24]  Jürgen Ziegler,et al.  Blended Recommending: Integrating Interactive Information Filtering and Algorithmic Recommender Techniques , 2015, CHI.

[25]  Barbara Tversky,et al.  Some Ways that Maps and Diagrams Communicate , 2000, Spatial Cognition.

[26]  Jimmy J. Lin,et al.  Navigating information spaces: A case study of related article search in PubMed , 2008, Inf. Process. Manag..

[27]  Keith Cheverst,et al.  Providing Tailored (Context-Aware) Information to City Visitors , 2000, AH.

[28]  Grace Colby,et al.  Transparency and blur as selective cues for complex visual information , 1991, Electronic Imaging.

[29]  Mária Bieliková,et al.  Improving Semantic Search Via Integrated Personalized Faceted and Visual Graph Navigation , 2008, SOFSEM.

[30]  Robert R. Korfhage,et al.  Visualization of a Document Collection: The VIBE System , 1993, Inf. Process. Manag..

[31]  Giovanni Maria Sacco,et al.  Dynamic Taxonomies: A Model for Large Information Bases , 2000, IEEE Trans. Knowl. Data Eng..

[32]  Mária Bieliková,et al.  Generating Exploratory Search Interfaces for the Semantic Web , 2010, HCIS.

[33]  Desney S. Tan,et al.  FacetLens: exposing trends and relationships to support sensemaking within faceted datasets , 2009, CHI.

[34]  Lynda Hardman,et al.  /facet: A Browser for Heterogeneous Semantic Web Repositories , 2006, SEMWEB.

[35]  Yannis Tzitzikas,et al.  Hippalus: Preference-enriched Faceted Exploration , 2014, EDBT/ICDT Workshops.

[36]  Gennady L. Andrienko,et al.  Interactive maps for visual data exploration , 1999, Int. J. Geogr. Inf. Sci..