What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Database
His scientific interests lie mostly in Theoretical computer science, Data mining, Graph, Information retrieval and Artificial intelligence.
His studies deal with areas such as Graph, Graph partition, Null graph and Graph factorization as well as Theoretical computer science.
Haixun Wang works on Data mining which deals in particular with Data stream mining.
Haixun Wang focuses mostly in the field of Graph, narrowing it down to topics relating to Reachability and, in certain cases, Block graph, Bipartite graph, Transitive closure, Directed acyclic graph and Directed graph.
His work on RDF, Topic model and Search engine indexing as part of his general Information retrieval study is frequently connected to Noisy text analytics, thereby bridging the divide between different branches of science.
His work deals with themes such as Machine learning, Pattern recognition and Natural language processing, which intersect with Artificial intelligence.
His most cited work include:
- Mining concept-drifting data streams using ensemble classifiers (1075 citations)
- Probase: a probabilistic taxonomy for text understanding (575 citations)
- BLINKS: ranked keyword searches on graphs (507 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Data mining, Artificial intelligence, Information retrieval, Theoretical computer science and Data stream mining.
His studies in Data mining integrate themes in fields like Data set, Set, Search engine indexing and Cluster analysis.
His Artificial intelligence research incorporates themes from Machine learning, Pattern recognition and Natural language processing.
His Information retrieval study frequently links to other fields, such as Web page.
His Theoretical computer science study integrates concerns from other disciplines, such as Algorithm design and Graph database, Graph.
The study incorporates disciplines such as Data stream, SQL, Knowledge extraction and Concept mining in addition to Data stream mining.
He most often published in these fields:
- Data mining (30.74%)
- Artificial intelligence (26.15%)
- Information retrieval (21.55%)
What were the highlights of his more recent work (between 2013-2020)?
- Artificial intelligence (26.15%)
- Natural language processing (10.60%)
- Semantics (8.13%)
In recent papers he was focusing on the following fields of study:
Haixun Wang spends much of his time researching Artificial intelligence, Natural language processing, Semantics, Natural language and Information retrieval.
Haixun Wang has researched Artificial intelligence in several fields, including Text mining and Machine learning.
His studies deal with areas such as Question answering, Database and Knowledge base as well as Natural language.
His Information retrieval research is multidisciplinary, incorporating perspectives in Data science, Knowledge extraction, Cluster analysis and Knowledge engineering.
His Semantic change research includes elements of Relationship extraction and Data mining.
His study in Data mining is interdisciplinary in nature, drawing from both Theoretical computer science and Data set.
Between 2013 and 2020, his most popular works were:
- Natural language question answering over RDF: a graph data driven approach (180 citations)
- Local search of communities in large graphs (148 citations)
- KBQA: learning question answering over QA corpora and knowledge bases (105 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Haixun Wang mainly focuses on Artificial intelligence, Natural language processing, Knowledge base, Information retrieval and Semantics.
His study looks at the relationship between Artificial intelligence and topics such as Machine learning, which overlap with Identification.
The concepts of his Natural language processing study are interwoven with issues in Domain, Semantic computing and Conceptualization.
His Knowledge base research incorporates elements of RDF, Graph based, Question answering, RDF query language and Natural language.
In his study, Cluster analysis, Text retrieval, Deep learning and Hash function is strongly linked to Text processing, which falls under the umbrella field of Information retrieval.
His Graph research is multidisciplinary, relying on both Subgraph isomorphism problem, Graph and Theoretical computer science.
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