What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Database
His primary areas of study are Semantic Web, Description logic, Ontology, Information retrieval and Artificial intelligence.
His biological study spans a wide range of topics, including Semantics and Programming language.
His research integrates issues of Axiom, Extension, Fuzzy logic and Knowledge representation and reasoning in his study of Description logic.
Ontology is closely attributed to Operator in his work.
Jeff Z. Pan combines subjects such as Knowledge compilation and Benchmark with his study of Information retrieval.
His Artificial intelligence research includes themes of Classical logic and Natural language processing.
His most cited work include:
- The Semantic Web - Iswc 2010 (278 citations)
- An Argument-Based Approach to Using Multiple Ontologies (244 citations)
- Fuzzy OWL: Uncertainty and the Semantic Web. (166 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Ontology, Semantic Web, Description logic, Artificial intelligence and Information retrieval.
His research investigates the connection between Ontology and topics such as Semantics that intersect with issues in Syntax.
The subject of his Semantic Web research is within the realm of World Wide Web.
His Description logic study combines topics from a wide range of disciplines, such as Inference, Data mining and Fuzzy logic.
His work deals with themes such as Identification, Machine learning, Concept drift and Natural language processing, which intersect with Artificial intelligence.
His Information retrieval study integrates concerns from other disciplines, such as Task and Natural language.
He most often published in these fields:
- Ontology (35.57%)
- Semantic Web (37.61%)
- Description logic (26.24%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (26.53%)
- Information retrieval (25.66%)
- Knowledge graph (9.91%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Information retrieval, Knowledge graph, Semantic Web and Question answering.
He combines subjects such as Machine learning and Natural language processing with his study of Artificial intelligence.
His Information retrieval research includes elements of Social media and Presentation.
His Knowledge graph research is multidisciplinary, incorporating perspectives in Graph, Theoretical computer science, Correctness, Approximate reasoning and Component.
Jeff Z. Pan has researched Semantic Web in several fields, including Consistency, Logical consequence and Cartography.
Jeff Z. Pan studies Web Ontology Language, a branch of Ontology.
Between 2017 and 2021, his most popular works were:
- Content Based Fake News Detection Using Knowledge Graphs (37 citations)
- Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network (19 citations)
- Commonsense Properties from Query Logs and Question Answering Forums (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Database
Jeff Z. Pan mainly investigates Information retrieval, Question answering, Natural language, Semantic Web and Knowledge graph.
Jeff Z. Pan has included themes like Small number, Linear model and Ambiguity in his Information retrieval study.
He works mostly in the field of Natural language, limiting it down to concerns involving Task and, occasionally, Feature, Domain and Artificial intelligence.
He does research in Semantic Web, focusing on Semantic search specifically.
His work focuses on many connections between Logical consequence and other disciplines, such as Statement, that overlap with his field of interest in Ontology.
The Ontology study combines topics in areas such as Description logic, Representation, Usability and Transfer of learning, Machine 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.
