D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 68 Citations 22,115 250 World Ranking 952 National Ranking 571

Research.com Recognitions

Awards & Achievements

2021 - IEEE Fellow For contributions to machine learning and reasoning under uncertainty

2019 - ACM Fellow For contributions to machine learning, reasoning under uncertainty, and responsible data science

2013 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to methods which combine probabilistic and logical representations in machine learning, knowledge discovery, graph mining, network analysis, and database systems.

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Machine learning
  • Programming language

Lise Getoor spends much of her time researching Artificial intelligence, Probabilistic logic, Machine learning, Data mining and Statistical relational learning. Lise Getoor interconnects Natural language processing, Structure and Social network in the investigation of issues within Artificial intelligence. Her study in Probabilistic logic is interdisciplinary in nature, drawing from both Probabilistic database, Graphical model, Relational database and Inference.

In her research, Graph theory, Network classification and Viral marketing is intimately related to Collective classification, which falls under the overarching field of Machine learning. The various areas that Lise Getoor examines in her Data mining study include Domain, Graph, Information extraction, Information retrieval and Resolution. Her work deals with themes such as Online machine learning, Unsupervised learning, Instance-based learning, Structured prediction and Relational model, which intersect with Statistical relational learning.

Her most cited work include:

  • Collective Classification in Network Data (1279 citations)
  • Introduction to statistical relational learning (1016 citations)
  • Learning Probabilistic Relational Models (957 citations)

What are the main themes of her work throughout her whole career to date?

Lise Getoor mainly investigates Artificial intelligence, Probabilistic logic, Machine learning, Data mining and Inference. Lise Getoor has researched Artificial intelligence in several fields, including Graph, Social network, Natural language processing, Relational model and Statistical relational learning. She has included themes like Relational calculus, Data science and Logical conjunction in her Statistical relational learning study.

Her Probabilistic logic research is multidisciplinary, incorporating perspectives in Probabilistic database, Scalability, Theoretical computer science and Graph. As a part of the same scientific family, Lise Getoor mostly works in the field of Data mining, focusing on Information retrieval and, on occasion, Cluster analysis. Her study looks at the relationship between Inference and topics such as Graphical model, which overlap with Markov chain.

She most often published in these fields:

  • Artificial intelligence (36.36%)
  • Probabilistic logic (24.34%)
  • Machine learning (24.34%)

What were the highlights of her more recent work (between 2015-2020)?

  • Artificial intelligence (36.36%)
  • Probabilistic logic (24.34%)
  • Statistical relational learning (18.48%)

In recent papers she was focusing on the following fields of study:

Lise Getoor mainly focuses on Artificial intelligence, Probabilistic logic, Statistical relational learning, Inference and Machine learning. Her Natural language processing research extends to the thematically linked field of Artificial intelligence. Her Probabilistic logic study combines topics from a wide range of disciplines, such as Similarity, Theoretical computer science, Recommender system and Human–computer interaction.

The concepts of her Statistical relational learning study are interwoven with issues in Graphical model and Data science. Her biological study spans a wide range of topics, including Structure, Structured prediction and Data mining. Her studies in Data mining integrate themes in fields like Graph and Information retrieval.

Between 2015 and 2020, her most popular works were:

  • Hinge-loss Markov random fields and probabilistic soft logic (117 citations)
  • A probabilistic approach for collective similarity-based drug-drug interaction prediction. (46 citations)
  • Personalized explanations for hybrid recommender systems (44 citations)

In her most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Programming language

Her primary areas of study are Probabilistic logic, Artificial intelligence, Inference, Theoretical computer science and Machine learning. Her Probabilistic logic research includes elements of Graphical model, Web service, Human–computer interaction and Personalization. Lise Getoor combines subjects such as Intersection and Software system with her study of Artificial intelligence.

Her Inference research integrates issues from Affect, Structured prediction, Mathematical optimization, Generalization and Statistical relational learning. As part of the same scientific family, Lise Getoor usually focuses on Theoretical computer science, concentrating on Scalability and intersecting with Metadata. Lise Getoor works mostly in the field of Machine learning, limiting it down to topics relating to Similarity and, in certain cases, Transitive relation, as a part of the same area of interest.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Collective Classification in Network Data

Prithviraj Sen;Galileo Namata;Mustafa Bilgic;Lise Getoor.
Ai Magazine (2008)

1787 Citations

Introduction to statistical relational learning

Lise Getoor;Ben Taskar.
(2007)

1704 Citations

Learning Probabilistic Relational Models

Nir Friedman;Lise Getoor;Daphne Koller;Avi Pfeffer.
international joint conference on artificial intelligence (1999)

1443 Citations

Link mining: a survey

Lise Getoor;Christopher P. Diehl.
Sigkdd Explorations (2005)

1109 Citations

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)

Lise Getoor;Ben Taskar.
(2007)

775 Citations

Collective entity resolution in relational data

Indrajit Bhattacharya;Lise Getoor.
ACM Transactions on Knowledge Discovery From Data (2007)

762 Citations

To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles

Elena Zheleva;Lise Getoor.
the web conference (2009)

692 Citations

Link-based classification

Qing Lu;Lise Getoor.
international conference on machine learning (2003)

645 Citations

Preserving the privacy of sensitive relationships in graph data

Elena Zheleva;Lise Getoor.
knowledge discovery and data mining (2007)

520 Citations

A Latent Dirichlet Model for Unsupervised Entity Resolution

Indrajit Bhattacharya;Lise Getoor.
siam international conference on data mining (2005)

349 Citations

Best Scientists Citing Lise Getoor

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 88

Kristian Kersting

Kristian Kersting

TU Darmstadt

Publications: 80

Luc De Raedt

Luc De Raedt

KU Leuven

Publications: 67

Pedro Domingos

Pedro Domingos

University of Washington

Publications: 61

Jennifer Neville

Jennifer Neville

Purdue University West Lafayette

Publications: 59

Naren Ramakrishnan

Naren Ramakrishnan

Virginia Tech

Publications: 55

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 49

Charu C. Aggarwal

Charu C. Aggarwal

IBM (United States)

Publications: 43

Huan Liu

Huan Liu

Arizona State University

Publications: 41

David Jensen

David Jensen

University of Massachusetts Amherst

Publications: 39

Jie Tang

Jie Tang

Tsinghua University

Publications: 39

Christopher Ré

Christopher Ré

Stanford University

Publications: 38

Dan Suciu

Dan Suciu

University of Washington

Publications: 36

Lei Chen

Lei Chen

Hong Kong University of Science and Technology

Publications: 34

Volker Tresp

Volker Tresp

Ludwig-Maximilians-Universität München

Publications: 31

David Poole

David Poole

University of British Columbia

Publications: 31

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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