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Constructing knowledge graphs from heterogeneous data sources and evaluating their quality and consistency are important research questions in the field of knowledge graphs. We propose mapping rules to guide users to translate data from relational and graph sources into a meaningful knowledge graph and design a user-friendly language to specify the mapping rules. Given the mapping rules and constraints on source data, equivalent constraints on the target graph can be inferred, which is referred to as data source constraints. Besides this type of constraint, we design other two types: user-specified constraints and general rules that a high-quality knowledge graph should adhere to. We translate the three types of constraints into uniform expressions in the form of graph functional dependencies and extended graph dependencies, which can be used for consistency checking. Our approach provides a systematic way to build and evaluate knowledge graphs from diverse data sources. 

The successes of reinforcement learning in recent years are underpinned by the characterization of suitable reward functions. However, in settings where such rewards are non-intuitive, difficult to define, or otherwise error-prone in their definition, it is useful to instead learn the reward signal from expert demonstrations. This is the crux of inverse reinforcement learning (IRL). While eliciting learning requirements in the form of scalar reward signals has been shown to be effective, such representations lack explainability and lead to opaque learning. We aim to mitigate this situation by presenting a novel IRL method for eliciting declarative learning requirements in the form of a popular formal logic---Linear Temporal Logic (LTL)---from a set of traces given by the expert policy. 

In this paper, the effect of tactile affordance during the design of Extended Reality (XR) based environments is presented. Tactile affordance is one of the Human eXtended Reality Interaction (HXRI) criteria which help lay the foundation for human-centricXR-based training environments. XR-based training environments developed for two surgical procedures have been used to study the role of tactile affordance. The first XR environment is developed for the Condylar plating surgical procedure which is performed to treat the fractures of the femur bone and the second XR environment is developed to train users in endotracheal intubation. Three studies have been conducted to understand the influence of different interactionmethods to elevate tactile affordance in XR-based environments. The studies and the results of the studies have been exhaustively discussed in this paper.