No / We investigate how to provide personalized nudges to aid a user’s exploration of linked data in a way leading to expanding her domain knowledge. This requires a model of the user’s familiarity with domain concepts. The paper examines an approach to detect user domain familiarity by exploiting anchoring concepts which provide a backbone for probing interactions over the linked data graph. Basic level concepts studied in Cognitive Science are adopted. A user study examines how such concepts can be utilized to deal with the cold start user modelling problem, which informs a probing algorithm.

Yes / The recent growth of the Web of Data has brought to the fore the need to develop intelligent means to support user exploration through big data graphs. To be effective, approaches for data graph exploration should take into account the utility from a user’s point of view. We have been investigating knowledge utility – how useful the trajectories in a data graph are for expanding users’ knowledge. Following the theory for meaningful learning, according to which new knowledge is developed starting from familiar entities (anchors) and expanding to new and unfamiliar entities, we propose here an approach to identify knowledge anchors in a data graph. Our approach is underpinned by the Cognitive Science notion of basic level objects in domain taxonomies. Several metrics for extracting knowledge anchors in a data graph, and the corresponding algorithms, are presented. The metrics performance is examined, and a hybridization approach that combines the strengths of each metric is proposed.

Yes / This paper investigates how to facilitate users’ exploration through data graphs for knowledge expansion. Our work focuses on knowledge utility – increasing users’ domain knowledge while exploring a data graph. We introduce a novel exploration support mechanism underpinned by the subsumption theory of meaningful learning, which postulates that new knowledge is grasped by starting from familiar concepts in the graph which serve as knowledge anchors from where links to new knowledge are made. A core algorithmic component for operationalising the subsumption theory for meaningful learning to generate exploration paths for knowledge expansion is the automatic identification of knowledge anchors in a data graph (KADG). We present several metrics for identifying KADG which are evaluated against familiar concepts in human cognitive structures. A subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion is presented, and applied in the context of a Semantic data browser in a music domain. The resultant exploration paths are evaluated in a task-driven experimental user study compared to free data graph exploration. The findings show that exploration paths, based on subsumption and using knowledge anchors, lead to significantly higher increase in the users’ conceptual knowledge and better usability than free exploration of data graphs. The work opens a new avenue in semantic data exploration which investigates the link between learning and knowledge exploration. This extends the value of exploration and enables broader applications of data graphs in systems where the end users are not experts in the specific domain.