Blog – Page 2 – Data Science Research

Extracting Visual Knowledge from the Web with Multimodal Learning

May 26, 2017 Comment Closed publications, research directions

We consider the problem of automatically extracting visual objects from web images. Despite the extraordinary advancement in deep learning, visual object detection remains a challenging task. To overcome the deficiency of pure visual techniques, we propose to make use of meta text surrounding images on the Web for enhanced detection accuracy. In this work we present

Read more »
Interactive Inference for Information Extraction

March 14, 2017 Comment Closed Uncategorized

Introduction In order to keep up with the enormous pace of data being produced on the web, we need automatic methods for converting it into a more structured form for analyzing and querying. For text data this amounts to generating labels which describe the underlying semantic concept and is known as Information Extraction (IE). Examples

Read more »
SigmaKB VLDB16 Demo System and TAC KBP Slot Filling Validation 2016

December 20, 2016 Comment Closed Uncategorized

Miguel Rodriguez The amount of information available on the web has motivated a number of efforts in creating large-scale knowledge bases (KBs), each with their own methods of automatically extracting relevant information from unstructured text. Despite sharing the same data model, each project is unique, displaying their own strengths and weaknesses related to the size

Read more »
The ArchimedesOne Knowledge Base System

October 20, 2016 Comment Closed publications, research directions

Yang Chen, Xiaofeng Zhou Recent development in information extraction and data management systems arouses elevating efforts in constructing large knowledge bases (KBs). These knowledge bases store information in a structured format, facilitating efficient processing and querying. Examples of these knowledge bases include DBpedia, DeepDive, Freebase, Google Knowledge Graph, Knowledge Vault, NELL, OpenIE, ProBase, ProbKB, and

Read more »
NIST 2015 Knowledge Base Population – Ensemble

April 15, 2016 Comment Closed NIST and open eval, research directions

Miguel Rodriguez The Text Analysis Conference (TAC) is a series of evaluation workshops organized by NIST to encourage research in Natural Language Processing and related applications. TAC is focused on Knowledge Base Population (KBP), automated systems that discover information about entities found in a large corpus and incorporate them into a knowledge base. The TAC-KBP evaluation is composed

Read more »
NIST 2015 Pre-Pilot Data Science Evaluation

March 21, 2016 Comment Closed courses, NIST and open eval, research directions

Dihong Gong and Daisy Zhe Wang We participated in the 2015 Pre-Pilot Data Science Evaluation organized by the National Institute of Standards and Technology (NIST). The primary goal of the pre-pilot evaluation is to develop and exercise the evaluation process in the context of data science. The evaluation consists of four tasks including data cleaning,

Read more »
SMART Electronic Discovery: System Evaluation

December 27, 2015 Comment Closed research directions

Clint P. George This is an extension to our earlier post, SMART Electronic Discovery (SMARTeR), which describes a framework for electronic discovery (e-discovery). Discovery is a pre-trial procedure in a lawsuit or legal investigation in which each party can obtain evidence from other parties (typically via a request for production) according to the laws of civil procedure in the

Read more »
Multimodal Ensemble Fusion for Disambiguation and Retrieval

November 25, 2015 Comment Closed publications, research directions

Yang Peng Huge amount of multimedia data is generated on the Internet everyday. In order to utilize multimodal data, researchers have developed a lot of multimodal machine learning models to integrate data of multiple modailities, including text, images, audios and videos. In the multimedia analysis community, multimodal fusion is greatly employed for various multimedia analysis

Read more »
MADlib now an ASF incubator project! Do you have MAD Skills?

Daisy Zhe Wang October 16, 2015 Comment Closed publications, research directions

Daisy Zhe Wang MADlib is an open-source library (licensed under 2-clause BSD license) for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical and machine learning methods for structured and unstructured data. The MADlib mission is to foster widespread development of scalable analytic skills, by harnessing efforts from commercial practice, academic research, and open

Read more »
Query-Driven Sampling for Collective Entity Resolution

May 21, 2015 Comment Closed publications, research directions

Christan Grant One of the research theme of the UF Data Science Research lab is to adapt machine learning algorithm to real-time interactive query engines. Our prior work looked at implementing and optimizing information extraction and statistical machine learning operations in exploratory query processing systems. To push the envelop further, our newly completed paper (under-submission)

Read more »