Using Knowledge Graphs to Explore Propeganda
Using real declassified documents and spy novels
Steph Buongiorno, PhD, is a postdoctoral research fellow in data science and high-performance computing (HPC) at Emory University’s Department of Data and Decision Sciences. Her transdisciplinary research produces knowledge that extends beyond the limits of any single field, introducing new methods for interpreting texts, data, culture, and society.
She reads poetry, enjoys all sorts of sensory things, and explores quiet, underwater worlds.
GAME-KG
Knowledge graphs (KGs) can augment large language models (LLMs) while also providing an explainable set of facts that can be inspected by a human. Explainability is valuable for fields that may otherwise avoid LLMs due to hallucinations, such as human trafficking analysis. Creating KGs poses challenges, however. KGs parsed from documents may include explicit connections (those directly stated in a document) but miss implicit connections (those evident to a human, but not directly stated). This research introduces GAME-KG, an approach to modifying explicit and implicit KG connections by crowdsourcing feedback through video games.
Dark Shadows
Dark Shadows is a film-noir style detective thriller that acts as a test bed for proof-of-concept and prototype system components, frameworks, and models that contribute to research in AI and machine learning. The gameplay focuses on social scenarios where players provide natural language input to progress the narrative. Dark Shadows includes PANGeA’s novel validation system, which leverages self-reflection to evoke a large language model's (LLM) intelligence when evaluating and responding to user input. Narrative and artwork are procedurally generated.
Agent-Driven, Game-Based Learning: Personalized CS Education for Diverse Students
AI agents can personalize education by identifying students’ strengths, weaknesses, and personalities to generate content tailored to them. This work presents "personalized education agents" deployed in an educational version of Minecraft. Agents bridge concepts from lessons to "big picture" thinking by creating connections between STEM and interdisciplinary topics, such as the Language Arts. Agents translate student progression and learning outcomes to teachers for their assessment of student progress.
The Hansard Viewer Demo App
Use an array of data-mining and statistical approaches to gain new insights into the evolution and nature of political language as it occurs in different time periods and in different contexts.
rOpengov Universe
Open government data enables citizens to see government activities and decision-making processes, empowering them to participate more fully in the democratic process. Researchers can use open government data for studies and projects, generating insights and contributing to evidence-based policy-making. To this end, this work introduces R packages hosted by the rOpengov Universe that are designed to make analyzing contemporary and historical open government data more accessible. Queried data is returned in a clean and analysis-ready dataframe.
Course Materials: Digital History
Computational methods are changing the way that we access information about history and society. These methods help us to detect change over time, to identify influential figures, and to name turning points. What happens when we apply these tools to the entire Hansard corpus or to a million congressional debates and tweets? This work provides an introduction to the analytic methodologies transforming the humanities and social sciences via a book, under contract at Cambridge University Press, and series of Jupyter Notebooks aimed at exploring questions like these.
Syntactic Dependency Relationships and the Extraction of Grammatical Triples
This paper describes a method of triples extraction, posextract, which has been designed to meet the increasing need for high-accuracy triples outputs for the analysis of text.