by Aashish Dahwan
A UF Data Science Research Lab and Data Studio Lab collaboration placed second in all five language tracks — earning the highest overall score through consistent performance across Guaraní, Yucatec Maya, Nahuatl, Wixarika, and Bribri.
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HPCC Ecl Workshop
Tinghui Zhang Comment Closed Uncategorized
by Tinghui Zhang and Yifan Wang
During April 2-5, the Data Science Research (DSR) Lab at the University of Florida joined forces with HPCC Systems from LexisNexis to run a hands-on workshop focused on ECL (Enterprise Control Language). Over 20 students participated in this event to learn more about managing and analyzing big data. -
Improving Rare Tree Species Classification using Domain Knowledge
I. Harmon Comment Closed Uncategorized
by I. Harmon
Forests are an integral part of life on earth. They are important for carbon sequestration, fuel, building materials, and animal habitats, etc. Monitoring and managing ecosystem health requires forest management. Forest management includes doing field surveys in addition to -
Concept-centric Representation, Learning, Reasoning, and Interaction (CReLeRI)
I. Harmon Comment Closed Uncategorized
PI: Zhiting Hu (UCSD) Co-PIs: Jaime Ruiz (UF), Daisy Wang (UF), Eric Xing (CMU), Jun-Yan Zhu (CMU)
We propose the new perspective of Concept-centric Representation, Learning, Reasoning, and Interaction (CReLeRI) that addresses the above fundamental challenges with a suit of methodological innovations. -
DBSim: Extensible Database Simulator for Fast Prototyping In-Database Algorithms
Yifan Wang Comment Closed Uncategorized
by Yifan Wang
Many data scientists and analysts have to spend a large portion of time in a routine loop: exporting data from database, processing/analyzing the data using external data science tools, and re-importing the data back to database. To solve this problem and save users’ time, in-database analytics is emerging in recent years, which -
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries
Jayetri Bardhan Comment Closed Uncategorized
by Jayetri Bardhan
Introduction Electronic Health Records (EHRs) are digitized records of patients’ medical information containing details about their demographics, diagnoses, medication, symptoms, laboratory results, and immunization records. EHRs help doctors in making better clinical decisions and aid patients to obtain answers to patient-specific questions. EHRs may be in the form of structured or unstructured -
A Brief Overview of Weak Supervision
Ira Harmon Comment Closed research, survey
by I. Harmon
Introduction Machine learning has a growing influence on modern life. Machine learning models are used in autonomous vehicles, they’re trained to make clinical diagnoses from radiological images, and they’re used to make financial predictions. As machine learning becomes more prevalent, the consequences of errors within the model becomes more severe. With autonomous -
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
Comment Closed publications, research
by Ali Sadeghian
In this blog, we will learn about: Knowledge graphs Rule mining on KGs Differentiable rule mining (DRUM) Applications of DRUM Introduction AI systems can hugely benefit from incorporating knowledge that is often trivial for humans. For example, knowing al-Khwarizmi was born in Khawrazm, and Khawrazm was part of Persia; we can infer that al-Khwarizmi -
IDTrees Data Science Challenge: 2017
Ira Harmon Comment Closed ecology, research
The Importance of Forests Forests are one of humankind’s most valuable resources. They cover 30% of the Earth, providing habitats for wildlife, wood for buildings and fuel, and most importantly 20 – 50% of the Earth’s oxygen(Carlowicz, 2012). Governments around the world have recognized the importance of forests and in doing so have created institutions
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Efficient Conditional Rule Mining over Knowledge Bases
Comment Closed publications, research directions
Current web-scale knowledge bases (KBs) incorporate a substantial amount of information in a structured format. Availability of this readily machine-digestible data has made KBs a desirable resource for other applications. This has motivated many to explore learning on KBs. Graph embeddings and inference rule learning are examples such methods. This paper concerns the later, mostly because
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Taming The Data Monster To Make Better Decisions
Daisy Zhe Wang Comment Closed Uncategorized
[Source: News from The Herbert Wertheim College of Engineering] In today’s increasingly connected world, the sheer volume of messages we receive on any subject, including speech, images, video and metadata, as well as text imbedded in images and videos, is mind-boggling. When something unexpected happens, government policy analysts need to inform and advise our nation’s
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Mining Rules Incrementally over Large Knowledge Bases
Comment Closed publications, research directions
by Xiaofeng Zhou
Multiple web-scale knowledge bases (e.g., Freebase, YAGO, NELL) have been constructed using semi-supervised or unsupervised information extraction techniques and many of them, despite their large sizes, are continuously growing. Much research effort has been put into mining inference rules from these knowledge bases. To address the task of rule mining over evolving web-scale knowledge bases, -
Multimodal Learning for Web Information Extraction
Comment Closed publications
We consider the problem of extracting text instances of predefined categories (e.g. city and person) from the Web. Instances of a category may be scattered across thousands of independent sources in many different formats with potential noises, which makes open-domain information extraction a challenging problem. Learning syntactic rules like “cities such as _” or “_
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Archimedes: Efficient Query Processing over Probabilistic Knowledge Bases
Comment Closed publications, research directions
We present the ARCHIMEDES system for efficient query processing over probabilistic knowledge bases. We design ARCHIMEDES for knowledge bases containing incomplete and uncertain information due to limitations of information sources and human knowledge. Answering queries over these knowledge bases requires efficient probabilistic inference. In this paper, we describe ARCHIMEDES’s efficient knowledge expansion and query-driven inference over UDA-GIST, an