What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Yuefeng Li mostly deals with Information retrieval, Data mining, Ontology, Text mining and Ontology.
Yuefeng Li interconnects World Wide Web and Personalization in the investigation of issues within Information retrieval.
His Data mining study integrates concerns from other disciplines, such as Information filtering system and Web mining.
His Ontology research incorporates elements of Domain, Fuzzy set, Fuzzy logic and Search engine.
His Text mining study combines topics in areas such as The Internet, Polysemy, Artificial intelligence, Machine learning and Concept mining.
His studies deal with areas such as Web standards, Web design, Web mapping and Web modeling as well as Social Semantic Web.
His most cited work include:
- Effective Pattern Discovery for Text Mining (246 citations)
- Mining ontology for automatically acquiring Web user information needs (168 citations)
- The state-of-the-art in personalized recommender systems for social networking (138 citations)
What are the main themes of his work throughout his whole career to date?
Yuefeng Li focuses on Information retrieval, Data mining, Artificial intelligence, Ontology and Association rule learning.
Information retrieval is often connected to World Wide Web in his work.
The concepts of his Data mining study are interwoven with issues in Information filtering system, Set, Cluster analysis and Decision rule.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Pattern recognition and Natural language processing.
His research in the fields of Upper ontology, Ontology-based data integration and Process ontology overlaps with other disciplines such as Ontology.
He has included themes like Quality and Association in his Association rule learning study.
He most often published in these fields:
- Information retrieval (46.91%)
- Data mining (29.64%)
- Artificial intelligence (22.15%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (22.15%)
- Topic model (11.40%)
- Information retrieval (46.91%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Topic model, Information retrieval, Feature selection and Ontology.
His research integrates issues of Machine learning and Natural language processing in his study of Artificial intelligence.
His Topic model study combines topics in areas such as Baseline, Set, Relevance, Text mining and Discriminative model.
His biological study spans a wide range of topics, including Semantic similarity and Sensor fusion.
His Information retrieval study combines topics from a wide range of disciplines, such as Quality, Context, Representation, Selection and Microblogging.
His work on Ontology learning is typically connected to Ontology and Matching as part of general Ontology study, connecting several disciplines of science.
Between 2015 and 2021, his most popular works were:
- Enhancing Binary Classification by Modeling Uncertain Boundary in Three-Way Decisions (29 citations)
- Effective pseudo-relevance for Microblog retrieval (12 citations)
- Dual pattern-enhanced representations model for query-focused multi-document summarisation (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
The scientist’s investigation covers issues in Information retrieval, Topic model, Discriminative model, Relevance and Context.
His Information retrieval research is mostly focused on the topic Ontology.
While the research belongs to areas of Topic model, Yuefeng Li spends his time largely on the problem of Information needs, intersecting his research to questions surrounding DUAL, Sentence, Data modeling, Personalization and Ontology learning.
His Relevance research is multidisciplinary, incorporating perspectives in Polysemy, Data mining and Feature selection.
The Data mining study combines topics in areas such as Cluster analysis and Dynamic topic model.
His Context research includes themes of Word embedding, Artificial intelligence and Natural language processing.
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