Combining Serendipity and Active Learning for Personalized Contextual Exploration of Knowledge Graphs

Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applications for the contextual exploration of KGs help users explore information extracted from a KG, including SAs, while they are reading an input text. Because of the large number of SAs that can be extracted from a text, a first challenge in these applications is to effectively determine which SAs are most interesting to the users, defining a suitable ranking function over SAs. However, since different users may have different interests, an additional challenge is to personalize this ranking function to match individual users’ preferences. In this paper we introduce a novel active learning to rank model to let a user rate small samples of SAs, which are used to iteratively learn a personalized ranking function. Experiments conducted with two data sets show that the approach is able to improve the quality of the ranking function with a limited number of user interactions.