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 42 Citations 9,009 214 World Ranking 4188 National Ranking 248

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Programming language
  • Machine learning

Thomas Lukasiewicz mainly focuses on Description logic, Probabilistic logic, Theoretical computer science, Semantic Web and Artificial intelligence. His Description logic study incorporates themes from Ontology, Ontology language, Well-founded semantics and Conjunctive query. The Probabilistic logic study combines topics in areas such as Algorithm, Rotation formalisms in three dimensions and Preferential entailment.

His work on Computational logic as part of general Theoretical computer science research is often related to T-norm fuzzy logics, thus linking different fields of science. His research on Semantic Web concerns the broader Information retrieval. In his work, Web Ontology Language and Range is strongly intertwined with Natural language processing, which is a subfield of Artificial intelligence.

His most cited work include:

  • Managing uncertainty and vagueness in description logics for the Semantic Web (426 citations)
  • Combining answer set programming with description logics for the Semantic Web (330 citations)
  • A general Datalog-based framework for tractable query answering over ontologies (328 citations)

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

His main research concerns Artificial intelligence, Theoretical computer science, Probabilistic logic, Description logic and Semantic Web. His Artificial intelligence research incorporates themes from Machine learning and Natural language processing. His work investigates the relationship between Theoretical computer science and topics such as Well-founded semantics that intersect with problems in Semantics of logic and Logic programming.

Thomas Lukasiewicz has researched Probabilistic logic in several fields, including Algorithm and Logical consequence. His research integrates issues of Ontology, Ontology language, Conjunctive query and Decidability in his study of Description logic. His work in Information retrieval addresses subjects such as Datalog, which are connected to disciplines such as Data integration and Time complexity.

He most often published in these fields:

  • Artificial intelligence (31.50%)
  • Theoretical computer science (31.21%)
  • Probabilistic logic (27.46%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (31.50%)
  • Machine learning (9.54%)
  • Natural language processing (8.09%)

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

His scientific interests lie mostly in Artificial intelligence, Machine learning, Natural language processing, Natural language and Deep learning. His studies deal with areas such as Task and Pattern recognition as well as Artificial intelligence. As a part of the same scientific study, Thomas Lukasiewicz usually deals with the Task, concentrating on Multi-label classification and frequently concerns with Theoretical computer science.

His research on Theoretical computer science frequently connects to adjacent areas such as Initialization. His Natural language processing research includes elements of Commonsense reasoning, Existentialism and Coreference. Thomas Lukasiewicz has researched Artificial neural network in several fields, including Time complexity, Algorithm and Probabilistic logic.

Between 2018 and 2021, his most popular works were:

  • A Surprisingly Robust Trick for the Winograd Schema Challenge (58 citations)
  • Controllable Text-to-Image Generation (44 citations)
  • ManiGAN: Text-Guided Image Manipulation (20 citations)

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

  • Artificial intelligence
  • Programming language
  • Machine learning

His primary scientific interests are in Artificial intelligence, Natural language processing, Image, Natural language understanding and Natural language. He interconnects Machine learning and Task in the investigation of issues within Artificial intelligence. Thomas Lukasiewicz has included themes like Class and Textual entailment, Logical consequence in his Natural language processing study.

His research in Natural language understanding tackles topics such as Commonsense reasoning which are related to areas like Inference, Language model and Commonsense knowledge. His Natural language research integrates issues from Adversarial system, Simple, Cognitive science and Word error rate. The study incorporates disciplines such as Time complexity, Algorithm and Probabilistic inference in addition to Deep learning.

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

Combining answer set programming with description logics for the Semantic Web

Thomas Eiter;Giovambattista Ianni;Thomas Lukasiewicz;Roman Schindlauer.
Artificial Intelligence (2008)

702 Citations

Combining answer set programming with description logics for the semantic web

Thomas Eiter;Thomas Lukasiewicz;Roman Schindlauer;Hans Tompits.
principles of knowledge representation and reasoning (2004)

636 Citations

Managing uncertainty and vagueness in description logics for the Semantic Web

Thomas Lukasiewicz;Umberto Straccia.
Journal of Web Semantics (2008)

601 Citations

A general Datalog-based framework for tractable query answering over ontologies

Andrea Calí;Georg Gottlob;Thomas Lukasiewicz.
Journal of Web Semantics (2012)

569 Citations

Expressive probabilistic description logics

Thomas Lukasiewicz.
Artificial Intelligence (2008)

298 Citations

Proceedings of the 7th International Symposium on the Foundations of Information and Knowledge Systems‚ FoIKS 2012‚ Kiel‚ Germany‚ March 5−9‚ 2012

Thomas Lukasiewicz.
(2000)

280 Citations

P-SHOQ(D): A Probabilistic Extension of SHOQ(D) for Probabilistic Ontologies in the Semantic Web

Rosalba Giugno;Thomas Lukasiewicz.
european conference on logics in artificial intelligence (2002)

247 Citations

Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications

Andrea Calì;Georg Gottlob;Thomas Lukasiewicz;Bruno Marnette.
logic in computer science (2010)

160 Citations

Datalog±: a unified approach to ontologies and integrity constraints

Andrea Calì;Georg Gottlob;Thomas Lukasiewicz.
international conference on database theory (2009)

153 Citations

Probabilistic Logic Programming

Thomas Lukasiewicz.
european conference on artificial intelligence (1998)

142 Citations

Best Scientists Citing Thomas Lukasiewicz

Umberto Straccia

Umberto Straccia

National Research Council (CNR)

Publications: 86

Thomas Eiter

Thomas Eiter

TU Wien

Publications: 73

Georg Gottlob

Georg Gottlob

University of Oxford

Publications: 59

Ian Horrocks

Ian Horrocks

University of Oxford

Publications: 32

Martine De Cock

Martine De Cock

University of Washington

Publications: 27

Pascal Hitzler

Pascal Hitzler

Kansas State University

Publications: 27

Riccardo Rosati

Riccardo Rosati

Sapienza University of Rome

Publications: 26

Nicola Leone

Nicola Leone

University of Calabria

Publications: 25

V. S. Subrahmanian

V. S. Subrahmanian

Dartmouth College

Publications: 23

Michael Zakharyaschev

Michael Zakharyaschev

Birkbeck, University of London

Publications: 23

Jeff Z. Pan

Jeff Z. Pan

University of Edinburgh

Publications: 22

Axel Polleres

Axel Polleres

Vienna University of Economics and Business

Publications: 22

Heiner Stuckenschmidt

Heiner Stuckenschmidt

University of Mannheim

Publications: 20

Sebastian Rudolph

Sebastian Rudolph

TU Dresden

Publications: 20

Henri Prade

Henri Prade

Paul Sabatier University

Publications: 20

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.

If you think any of the details on this page are incorrect, let us know.

Contact us
Something went wrong. Please try again later.