Projects

Archimedes: A Probabilistic Master Knowledge Base System

The Archimedes project aims at building a probabilistic master knowledge base system by combining novel system components and algorithms that we are designing and building at UF. In the context of the Archimedes project, we pursue a spectrum of research directions we are exploring at the UF Data Science Research (DSR) group including: query-driven and scalable statistical inference, probabilistic data models, state-parallel and data parallel data analytics framework, multimodal (e.g., text, image) information extraction, and KB schema enrichment minecraft fortnite herunterladen. This line of research on supporting large-scale automatically extracted knowledge bases is of high impact for many application domains from medical informatics to ecology zuma games for free. We have received funding from industry as well as federal government including DARPA, EMC, Amazon and Google. Other related projects include DeepDive from Stanford, YAGO from Max Planck Institute, NELL from CMU as well as WikiData/Freebase and Google Knowledge Vault herunterladen.

ProbKB	Large-scale Probabilistic Reasoning over Uncertain Knowledge Bases
HypoGator	Distinct Hypotheses and Claims Retrieval with Stance Detection on Controversial topics
DBlytics/MADLib	Textual Retrieval/Analytics in distributed MPP frameworks over hybrid hardware
Archer	Query-Driven Machine Learning
CAMeL	Leverage Crowd Support in Probabilistic Databases
SigmaKB	Knowledge fusion, cleaning and knowledge base integration
Rose	Knowledge Extraction and Exchange from Electronic Health Records
SMARTeR	Smarter information retrieval system
VITA	Multimodal knowledge extraction and fusion.