Combining Serendipity and Active Learning for Personalized Exploration of Knowledge Graphs

Tracking #: 2176-3389

This paper is currently under review
Authors: 
Federico Bianchi
Matteo Palmonari
Marco Cremaschi1
Elisabetta Fersini

Responsible editor: 
Jens Lehmann

Submission type: 
Full Paper
Abstract: 
Knowledge Graphs (KG) are now a widely used knowledge representation method and contain a large number of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between entities. Information that comes from a KG can be used to help a user that is doing a familiar task like reading an online news article, by adding contextual information that can provide informative background or serendipitous new details. Because of the large number of SAs that can be extracted from the entities that are found in an article, it is difficult to provide to the user the information that she needs. Moreover, different users might want to explore different SAs and thus exploration should be personalized. In this paper, we propose a method based on the combination between a heuristic measure, namely serendipity, and an active learning to rank algorithm that is used to learn a personalized ranking function for each user; this method asks the user to iteratively score small samples of SAs to learn the ranking function while reducing the effort on the user side. We conducted user studies in which users rate SAs while reading an online news article and used this data to run an experimental evaluation. We provide evidence that users are interested in different kinds of SAs, proving that personalization in this context is needed. Moreover, results not only show that our methodology provides an effective way to learn a personalized ranking function but also that this contextual exploration setting can help users learn new things.
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Under Review