'Entity resolution' presentation slideshows

Graph Identification

1.08k views • 83 slides

Graph Identification

Graph Identification. Lise Getoor University of Maryland, College Park. Some Acknowledgements. Students: Collaborators: Funding Sources:. Indrajit Bhattacharya Mustafa Bilgic Rezarta Islamaj Louis Licamele Qing Lu Galileo Namata Vivek Sehgal Prithvi Sen Elena Zheleva.

821 views • 65 slides

Graph Identification

Graph Identification. Lise Getoor University of Maryland, College Park. Some Acknowledgements. Students: Collaborators: Funding Sources:. Indrajit Bhattacharya Mustafa Bilgic Rezarta Islamaj Louis Licamele Qing Lu Galileo Namata Vivek Sehgal Prithvi Sen Elena Zheleva.

760 views • 65 slides

Machine Reading of Web Text

675 views • 53 slides

Collective Relational Clustering

533 views • 53 slides

Paul Kantor Rutgers May 14, 2007

701 views • 39 slides

E ntity R esolution in N etwork D ata

E ntity R esolution in N etwork D ata. Lise Getoor University of Maryland, College Park. Entity Resolution . The Problem The Algorithms Graph-based Clustering (GBC) Probabilistic Model (LDA-ER) The Tool The Big Picture. The Entity Resolution Problem. James Smith. John Smith.

371 views • 27 slides

Overcoming the Quality Curse

Overcoming the Quality Curse. Sharad Mehrotra University of California, Irvine. Collaborators/Students (Current) Dmitri Kalashnikov, Yasser Altowim, Hotham Altwaijry, Jeffrey Xu, Liyan Zhang Alumini Stella Zhaoqi Chen, Rabia Nuray-Turan, Virag Kothari. Beyond DASFAA 2003 paper.

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Video Entity Resolution: Applying ER Techniques for Smart Video Surveillance

Video Entity Resolution: Applying ER Techniques for Smart Video Surveillance. Liyan Zhang, Ronen Vaisenberg , Sharad Mehrotra , Dmitri V. Kalashnikov Department of Computer Science University of California, Irvine This material is based upon work supported by the NSF grants. Outline.

424 views • 25 slides

Video Entity Resolution: Applying ER Techniques for Smart Video Surveillance

Video Entity Resolution: Applying ER Techniques for Smart Video Surveillance. Liyan Zhang, Ronen Vaisenberg , Sharad Mehrotra , Dmitri V. Kalashnikov Department of Computer Science University of California, Irvine This material is based upon work supported by the NSF grants. Outline.

253 views • 25 slides

Overcoming the Quality Curse

Overcoming the Quality Curse. Sharad Mehrotra University of California, Irvine. Collaborators/Students (Current) Dmitri Kalashnikov, Yasser Altowim, Hotham Altwaijry, Jeffrey Xu, Liyan Zhang Alumini Stella Zhaoqi Chen, Rabia Nuray-Turan, Virag Kothari. Beyond DASFAA 2003 paper.

253 views • 25 slides

429 views • 23 slides

Using Transliteration with Entity Resolution for Arabic Datasets

Using Transliteration with Entity Resolution for Arabic Datasets. Marwah Alian Ghazi Al- Naymat Banda Ramadan. Real Time Entity Resolution. Entity Resolution : is the process of Identifying records that represents the same real world entity. Real world entity could be:

238 views • 23 slides

Exploiting Context Analysis for Combining Multiple Entity Resolution Systems

Exploiting Context Analysis for Combining Multiple Entity Resolution Systems. Zhaoqi Chen Dmitri V.kalashnikov Sharad Mehrotra. -Ramu Bandaru. Table of Contents. Introduction Related Work Problem Definition

345 views • 21 slides

An Overview of Data Analytics at DIMACS and DyDAn

An Overview of Data Analytics at DIMACS and DyDAn. Paul Kantor Fred Roberts. What is DIMACS?. DIMACS was formed as an NSF Science and Technology center in 1989 to foster research & education programs at the interface between discrete math and theoretical computer science

301 views • 19 slides

321 views • 17 slides

SRL: The Next Decade

SRL: The Next Decade. Lise Getoor University of Maryland, College Park. Past: Focus on Representations. LBN. RBN. SRM. PRISM. RDBN. RPM. CLP(BN). SLR. BLOG. PLL. pRN. PER. SLP. PRM. MLN. HMRF. RMN. RNM. DAPER. RDBN. RDN. BLP. SGLR. Present: Focus on Tasks.

203 views • 12 slides

SRL: The Next Decade

SRL: The Next Decade. Lise Getoor University of Maryland, College Park. Past: Focus on Representations. LBN. RBN. SRM. PRISM. RDBN. RPM. CLP(BN). SLR. BLOG. PLL. pRN. PER. SLP. PRM. MLN. HMRF. RMN. RNM. DAPER. RDBN. RDN. BLP. SGLR. Present: Focus on Tasks.

127 views • 12 slides

Statistische Methoden in der Computerlinguistik Statistical Methods in Computational Linguistics

407 views • 8 slides

Crawling, extraction and pre-processing

Crawling, extraction and pre-processing. Crawling and Extraction. Linked Data Similar to HTML crawling, but the the crawler needs to parse RDF/XML (and others) to extract URIs to be crawled Crawling of complete datasets can be faster using Semantic Sitemap / VOID descriptions

227 views • 7 slides