Using preference-enriched faceted search for species identification

Abstract

Species identification is essentially a decision-making process comprising steps in which the user makes a selection of characters, figures or photographs, or provides an input, that restricts other choices, until reaching one species. In some identification methods such decisions should have a specific order. Consequently, a wrong decision at the beginning of the process, could exclude a big set of options. To make this process more flexible and less vulnerable to wrong decisions, in this paper we investigate how a Preference-enriched Faceted Search (PFS) process can be used to aid the identification of species. We show how the proposed process covers and advances the existing methods and we report our experience from applying this process over data taken from FishBase. In the sequent, we elaborate on evaluation and we report the results of a task-based evaluation that shows that the PFS-based method can be used effectively by casual users.

Efficiently Charting RDF

“…Faceted Browsing: Among the most popular approaches that have been studied for exploring knowledge graphs is that of faceted browsing, where users incrementally add restrictions -called facets -to restrict the current results [72]. Early works mainly focused on smaller, domain-specific graphs, among which we mention the mSpace system [68] in the multimedia domain, BrowseRDF [60] in the crime domain, /facet [43] and Ontogator [52] in the art domain, or more recently, ReVeaLD [48] in the biomedical domain, and Hippalus [71] in the zoology domain. Such works typically have dealt with smaller-scale and/or homogeneous graphs with few classes and properties, focusing on usability and expressivity rather than issues of scale or performance.…”