Fostering parent–child dialog through automated discussion suggestions

The development of early literacy skills has been critically linked to a child’s later academic success. In particular, repeated studies have shown that reading aloud to children and providing opportunities for them to discuss the stories that they hear is of utmost importance to later academic success. CloudPrimer is a tablet-based interactive reading primer that aims to foster early literacy skills by supporting parents in shared reading with their children through user-targeted discussion topic suggestions. The tablet application records discussions between parents and children as they read a story and, in combination with a common sense knowledge base, leverages this information to produce suggestions. Because of the unique challenges presented by our application, the suggestion generation method relies on a novel topic modeling method that is based on semantic graph topology. We conducted a user study in which we compared how delivering suggestions generated by our approach compares to expert-crafted suggestions. Our results show that our system can successfully improve engagement and parent–child reading practices in the absence of a literacy expert’s tutoring.National Science Foundation (U.S.) (Award Number 1117584

NarDis:Narrativizing Disruption -How exploratory search can support media researchers to interpret ‘disruptive’ media events as lucid narratives

This project investigates how CLARIAH’s exploratory search and linked open data (LO D) browser DIVE+ supports media researchers to construct narratives about events, especially ‘disruptive’ events such as terrorist attacks and natural disasters. This project approaches this question by conducting user studies to examine how researchers use and create narratives with exploratory search tools, particularly DIVE+, to understand media events. These user studies were organized as workshops (using co-creation as an iterative approach to map search practices and storytelling data, including: focus groups & interviews; tasks & talk aloud protocols; surveys/questionnaires; and research diaries) and included more than 100 (digital) humanities researchers across Europe. Insights from these workshops show that exploratory search does facilitate the development of new research questions around disruptive events. DIVE+ triggers academic curiosity, by suggesting alternative connections between entities. Beside learning about research practices of (digital) humanities researchers and how these can be supported with digital tools, the pilot also culminated in improvements to the DIVE+ browser. The pilot helped optimize the browser’s functionalities, making it possible for users to annotate paths of search narratives, and save these in CLARIAH’s overarching, personalised, user space. The pilot was widely promoted at (inter)national conferences, and DIVE+ won the international LO DLAM (Linked Open Data in Libraries, Archives and Museums) Challenge Grand Prize in Venice (2017)

Similarity Reasoning over Semantic Context-Graphs

Similarity is a central cognitive mechanism for humans which enables a broad range of perceptual and abstraction processes, including recognizing and categorizing objects, drawing parallelism, and predicting outcomes. It has been studied computationally through models designed to replicate human judgment. The work presented in this dissertation leverages general purpose semantic networks to derive similarity measures in a problem-independent manner. We model both general and relational similarity using connectivity between concepts within semantic networks. Our first contribution is to model general similarity using concept connectivity, which we use to partition vocabularies into topics without the need of document corpora. We apply this model to derive topics from unstructured dialog, specifically enabling an early literacy primer application to support parents in having better conversations with their young children, as they are using the primer together. Second, we model relational similarity in proportional analogies. To do so, we derive relational parallelism by searching in semantic networks for similar path pairs that connect either side of this analogy statement. We then derive human readable explanations from the resulting similar path pair. We show that our model can answer broad-vocabulary analogy questions designed for human test takers with high confidence. The third contribution is to enable symbolic plan repair in robot planning through object substitution. When a failure occurs due to unforeseen changes in the environment, such as missing objects, we enable the planning domain to be extended with a number of alternative objects such that the plan can be repaired and execution to continue. To evaluate this type of similarity, we use both general and relational similarity. We demonstrate that the task context is essential in establishing which objects are interchangeable