A Survey of Definitions and Models of Exploratory Search

Abstract

International audienceExploratory search has an unclear and open-ended definition. The complexity of the task and the difficulty of defining this activity are reflected in the limits of existing evaluation methods for exploratory search systems. In order to improve them, we intend to design an evaluation method based on a user-centered model of exploratory search. In this work, we identified and defined the characteristics of exploratory search and used them as an information seeking model evaluation grid. We tested this analytic grid on two information seeking models: Ellis' and Marchionini's models. The results show that Marchonini's model does not match our evaluation method's requirements whereas on the other hand Ellis' model could be adapted to better suit exploratory search

A model of exploratory search for evaluating its systems & applications

“…First, we studied if an existing model could be the approximate model we were looking for. To do this examination, we used the acknowledged characteristics of ES proposed by [12] (see Table 1.A.) as an analysis grid.…”

A Model-based Heuristic Evaluation Method of Exploratory Search

“…The ten features of the model (from A to J) express typical ES behaviours such as having an evolving information need or a serendipitous attitude (Palagi, Gandon, Giboin, & Troncy, 2017): A. Define the search space; B.…”

Using knowledge anchors to facilitate user exploration of data graphs

“…In recent years, RDF linked data graphs have become widely available on the Web and are being adopted in a range of user-facing applications offering search and exploration tasks. In contrast to regular search where the user has a specific need in mind and an idea of the expected search result [1], exploratory search is open-ended requiring significant amount of exploration [2], has an unclear information need [3], and is used to conduct learning and investigative tasks [4]. There are numerous examples from exploring resources in a new domain (like in academic research tasks) to browsing through large information spaces with many options (like exploring job opportunities, travel and accommodation offers, videos, music).…”

“…It addresses a key challenge: how to support people who are not domain experts to explore data graphs in a way that can lead to expanding their domain knowledge. We investigate how to build automated ways for navigating through data graphs in order to add a new value to the exploration, which we call 'knowledge utility' -expanding one's domain knowledge while exploring a data graph 1 . Our earlier work showed that when exploring data graphs in unfamiliar or partially familiar domains, users serendipitously learn new things that they are unaware of [17][18][19].…”