What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Programming language
His primary scientific interests are in Distributed computing, Semantic grid, Semantic Web, World Wide Web and Personal knowledge management.
His study in Distributed computing is interdisciplinary in nature, drawing from both Process modeling, Workflow technology, Windows Workflow Foundation and Database.
His biological study spans a wide range of topics, including Semantics, Semantic similarity and Semantic computing.
His Semantic similarity study results in a more complete grasp of Information retrieval.
His Semantic Web research incorporates themes from Web resource, The Internet and Reference model.
His work deals with themes such as Domain knowledge and Knowledge engineering, which intersect with Personal knowledge management.
His most cited work include:
- Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning (250 citations)
- The knowledge grid (225 citations)
- Semantic linking through spaces for cyber-physical-socio intelligence: A methodology (197 citations)
What are the main themes of his work throughout his whole career to date?
Hai Zhuge mainly focuses on Information retrieval, Artificial intelligence, Knowledge management, Semantic grid and Data mining.
His Information retrieval research includes themes of Semantics and Graph.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Natural language processing.
His work in the fields of Knowledge management, such as Domain knowledge, Knowledge sharing, Personal knowledge management and Open Knowledge Base Connectivity, intersects with other areas such as Teamwork.
His studies in Semantic grid integrate themes in fields like Semantic computing, Semantic technology and Semantic network.
His Data mining study integrates concerns from other disciplines, such as Theoretical computer science and Cluster analysis.
He most often published in these fields:
- Information retrieval (31.84%)
- Artificial intelligence (22.86%)
- Knowledge management (12.65%)
What were the highlights of his more recent work (between 2015-2021)?
- Automatic summarization (12.24%)
- Information retrieval (31.84%)
- Artificial intelligence (22.86%)
In recent papers he was focusing on the following fields of study:
Hai Zhuge spends much of his time researching Automatic summarization, Information retrieval, Artificial intelligence, Semantic link and Semantics.
Hai Zhuge combines subjects such as Ranking, Graph and Dimension with his study of Information retrieval.
Hai Zhuge has researched Artificial intelligence in several fields, including Machine learning and Natural language processing.
His studies deal with areas such as Set, Association, Line, Key and Mechanism as well as Semantics.
Within one scientific family, Hai Zhuge focuses on topics pertaining to Cohesion under Cluster analysis, and may sometimes address concerns connected to Data mining.
To a larger extent, Hai Zhuge studies World Wide Web with the aim of understanding The Internet.
Between 2015 and 2021, his most popular works were:
- Multi-Dimensional Summarization in Cyber-Physical Society (44 citations)
- Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network (20 citations)
- Automatic generation of related work through summarizing citations (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Programming language
His primary areas of study are Information retrieval, Automatic summarization, Artificial intelligence, Semantic link and Machine learning.
His Information retrieval research includes elements of Ranking, Big data, Graph and Key.
His Automatic summarization research is multidisciplinary, relying on both Sentence, Semantic computing and Discriminative model.
Hai Zhuge has included themes like Abstraction, The Internet and Natural language processing in his Artificial intelligence study.
His Semantic link research is multidisciplinary, incorporating perspectives in Question answering, Lexical semantics, Semantic data model and Syntax.
His work carried out in the field of Machine learning brings together such families of science as Similarity and Cohesion.
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.
