What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Programming language
Her primary areas of investigation include Artificial intelligence, Bayesian network, Machine learning, Knowledge representation and reasoning and Probabilistic logic.
Her Artificial intelligence research incorporates themes from Probability distribution, Theoretical computer science and Process.
Her Theoretical computer science study integrates concerns from other disciplines, such as Generalization, Partition and Completeness.
Her research integrates issues of Evolutionary algorithm, Software engineering, Missing data and Search algorithm in her study of Bayesian network.
Her Machine learning research incorporates elements of Set and Data mining.
Her study looks at the intersection of Probabilistic logic and topics like Data science with Ontology, Human intelligence, Syntax, Semantic interoperability and Knowledge sharing.
Her most cited work include:
- Stochastic blockmodels: First steps (1636 citations)
- MEBN: A language for first-order Bayesian knowledge bases (202 citations)
- Sensitivity analysis for probability assessments in Bayesian networks (179 citations)
What are the main themes of her work throughout her whole career to date?
Kathryn B. Laskey mainly focuses on Artificial intelligence, Bayesian network, Machine learning, Probabilistic logic and Ontology.
Her research investigates the connection between Artificial intelligence and topics such as Process that intersect with issues in Knowledge base.
Her Bayesian network research incorporates themes from Probability distribution, Theoretical computer science, Random variable, Representation and Knowledge representation and reasoning.
Kathryn B. Laskey has included themes like Variable-order Bayesian network and Data mining in her Machine learning study.
She focuses mostly in the field of Probabilistic logic, narrowing it down to matters related to Data science and, in some cases, Interoperability and Decision support system.
Her biological study deals with issues like OWL-S, which deal with fields such as Semantic Web Rule Language.
She most often published in these fields:
- Artificial intelligence (44.17%)
- Bayesian network (28.33%)
- Machine learning (20.42%)
What were the highlights of her more recent work (between 2014-2020)?
- Artificial intelligence (44.17%)
- Bayesian network (28.33%)
- Inference (10.42%)
In recent papers she was focusing on the following fields of study:
Kathryn B. Laskey mainly focuses on Artificial intelligence, Bayesian network, Inference, Machine learning and Ontology.
Kathryn B. Laskey combines subjects such as Pattern recognition, Process and Natural language processing with her study of Artificial intelligence.
Her study in Bayesian network is interdisciplinary in nature, drawing from both Random variable, Knowledge representation and reasoning, Relational database, Use case and Sensor fusion.
Her Inference research integrates issues from Time complexity, Algorithm, Reduction, Insider threat and Data science.
Her research in Machine learning intersects with topics in Probability distribution and Test data generation.
Her Ontology study incorporates themes from Ontology, Key, Abstraction and Geolocation.
Between 2014 and 2020, her most popular works were:
- PR-OWL – a language for defining probabilistic ontologies (20 citations)
- Evaluation metrics for the practical application of URREF ontology: An illustration on data criteria (19 citations)
- Uncertainty representation, quantification and evaluation for data and information fusion (16 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Programming language
Kathryn B. Laskey focuses on Artificial intelligence, Ontology, Data mining, Machine learning and Bayesian network.
Artificial intelligence is closely attributed to Pattern recognition in her research.
Her Ontology study combines topics from a wide range of disciplines, such as Ontology, Sociotechnical system, Knowledge management and Natural language processing.
Kathryn B. Laskey has researched Data mining in several fields, including Data modeling, Uncertainty analysis and Rotation formalisms in three dimensions.
As part of the same scientific family, Kathryn B. Laskey usually focuses on Machine learning, concentrating on Process and intersecting with Ontology-based data integration and Process ontology.
Representation, Set, Conditional dependence and Use case is closely connected to Relational database in her research, which is encompassed under the umbrella topic of Bayesian network.
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