What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- World War II
- Statistics
The scientist’s investigation covers issues in Information retrieval, Citation, Data science, World Wide Web and Citation analysis.
He studies Information retrieval, focusing on Semantic Web in particular.
His Citation study incorporates themes from Bibliometrics, Rank and Similarity.
His studies deal with areas such as Chemical biology, Chemogenomics, Network theory, Network science and SPARQL as well as Data science.
Ying Ding works mostly in the field of World Wide Web, limiting it down to concerns involving Process ontology and, occasionally, OWL-S.
The Citation analysis study which covers Popularity that intersects with Social science and Receipt.
His most cited work include:
- Bibliometric cartography of information retrieval research by using co-word analysis (404 citations)
- Applying centrality measures to impact analysis: A coauthorship network analysis (273 citations)
- PageRank for ranking authors in co-citation networks (229 citations)
What are the main themes of his work throughout his whole career to date?
Ying Ding focuses on Information retrieval, World Wide Web, Data science, Semantic Web and Citation.
In his study, Word is inextricably linked to Field, which falls within the broad field of Information retrieval.
His work on World Wide Web is being expanded to include thematically relevant topics such as Ontology.
The Data science study combines topics in areas such as Topic model, Bibliometrics, Construct and Process.
Ying Ding is interested in Citation analysis, which is a field of Citation.
His Social Semantic Web study combines topics in areas such as Web standards, Semantic computing and Semantic Web Stack.
He most often published in these fields:
- Information retrieval (27.62%)
- World Wide Web (22.09%)
- Data science (20.64%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (13.08%)
- Graph (5.81%)
- Machine learning (5.52%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Graph, Machine learning, Data science and Embedding.
His research integrates issues of Field and Resource in his study of Artificial intelligence.
In Resource, Ying Ding works on issues like Metadata, which are connected to Information retrieval.
In his work, Raw data is strongly intertwined with Domain, which is a subfield of Information retrieval.
His Data science study combines topics from a wide range of disciplines, such as Knowledge graph and Drug repositioning.
The Science policy study combines topics in areas such as Process and Citation.
Between 2018 and 2021, his most popular works were:
- Comparison of humic and fulvic acid on remediation of arsenic contaminated soil by electrokinetic technology. (15 citations)
- edge2vec: Representation learning using edge semantics for biomedical knowledge discovery (15 citations)
- Analyzing linguistic complexity and scientific impact (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- World War II
- Statistics
Ying Ding mostly deals with Social media, Artificial intelligence, Stock market, Advertising and Social influence.
His Social media research spans across into fields like Public trust, Misinformation, China, Pandemic and Public relations.
His studies in Artificial intelligence integrate themes in fields like Random walk, Machine learning, Graph and Cascade.
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