Tootfinder

A Framework for Leveraging Human Computation Gaming to Enhance Knowledge Graphs for Accuracy Critical Generative AI Applications
Steph Buongiorno, Corey Clark
https://arxiv.org/abs/2404.19729 https://arxiv.org/pdf/2404.19729
arXiv:2404.19729v1 Announce Type: new
Abstract: External knowledge graphs (KGs) can be used to augment large language models (LLMs), while simultaneously providing an explainable knowledge base of facts that can be inspected by a human. This approach may be particularly valuable in domains where explainability is critical, like human trafficking data analysis. However, creating KGs can pose challenges. KGs parsed from documents may comprise explicit connections (those directly stated by a document) but miss implicit connections (those obvious to a human although not directly stated). To address these challenges, this preliminary research introduces the GAME-KG framework, standing for "Gaming for Augmenting Metadata and Enhancing Knowledge Graphs." GAME-KG is a federated approach to modifying explicit as well as implicit connections in KGs by using crowdsourced feedback collected through video games. GAME-KG is shown through two demonstrations: a Unity test scenario from Dark Shadows, a video game that collects feedback on KGs parsed from US Department of Justice (DOJ) Press Releases on human trafficking, and a following experiment where OpenAI's GPT-4 is prompted to answer questions based on a modified and unmodified KG. Initial results suggest that GAME-KG can be an effective framework for enhancing KGs, while simultaneously providing an explainable set of structured facts verified by humans.

KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering
Salvatore Bufi, Alberto Carlo Maria Mancino, Antonio Ferrara, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio
https://arxiv.org/abs/2403.20095

KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering
The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filtering (GCF). Following the same GNNs wave, recommender systems exploiting Knowledge Graphs (KGs) have also been successfully empowered by the GCF rationale to combine the representational power of GNNs with the semantics conveyed by KGs, giving rise to Knowledge-aware Graph Collaborative Filtering (KGCF), which use KGs to…

The dynamics of leadership and success in software development teams
Lorenzo Betti, Luca Gallo, Johannes Wachs, Federico Battiston
https://arxiv.org/abs/2404.18833

The dynamics of leadership and success in software development teams
From science to industry, teamwork plays a crucial role in knowledge production and innovation. Most studies consider teams as static groups of individuals, thereby failing to capture how the micro-dynamics of collaborative processes and organizational changes determine team success. Here, we leverage fine-grained temporal data on software development teams to gain insights into the dynamics of online collaborative projects. Our analysis reveals an uneven workload distribution in teams, with st…