Blog – Data Science Research

DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

Jayetri Bardhan September 3, 2022 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

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DBSim: Extensible Database Simulator for Fast Prototyping In-Database Algorithms

Yifan Wang August 31, 2022 Comment Closed publications, research

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 herunterladen. To solve this problem and save users’ time, in-database analytics is emerging in recent years,

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A Brief Overview of Weak Supervision

Ira Harmon October 16, 2020 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

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DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

December 16, 2019 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

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IDTrees Data Science Challenge: 2017

Ira Harmon April 1, 2019 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

December 12, 2018 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 August 17, 2018 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 yoga music for free. When something unexpected happens, government policy analysts need to inform

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Mining Rules Incrementally over Large Knowledge Bases

February 12, 2018 Comment Closed publications, research directions

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,

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Multimodal Learning for Web Information Extraction

November 8, 2017 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 issuu documents. Learning syntactic rules like “cities such as _”

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Archimedes: Efficient Query Processing over Probabilistic Knowledge Bases

June 26, 2017 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

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