In Faceted Search Systems (FSS), users navigate the information space through facets, which are attributes or meta-data that describe the underlying content of the collection. Type-based facets (aka t-facets) help explore the categories associated with the searched objects in structured information space. This work investigates how personalizing t-facets ranking can minimize user effort to reach the intended search target. We propose a lightweight personalisation method based on Vector Space Model (VSM) for ranking the t-facet hierarchy in two steps.
The National Geographic Institute of Spain (NGI) knowledge graph and semantic web is a project built with symbolic artificial intelligence based on a semantically interpreted knowledge graph, powered with GNOSS technology. The knowledge graph of the National Geographic Institute of Spain integrates more than 2 million geographical resources, coming from 3 different sources of NGI, described in a very expressive way and offers a set of interfaces for people that allow their discovery, exploration, navigation, search and visualization.